moonbit_constraint
Finite-domain constraint programming library for MoonBit
constraint-programming
finite-domain
scheduling
optimization
moon add mjfmjf879/moonbit_constraint@0.1.0
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0.1.0
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README#moonbit-constraint
面向组合配置、排班、资源分配与组合优化的纯 MoonBit 有限域约束求解库。
#项目定位
moonbit-constraint 提供有限整数域、可组合约束、确定性搜索、诊断统计和应用级建模组件，不依赖外部求解器运行时。
#核心能力
- 区间域、离散值域、域运算和边界操作。
- 等式/不等式、线性、AllDifferent、表约束、计数、极值、区间资源约束。
- MRV 等变量启发式、解枚举、节点预算、增量假设、诊断统计和确定性基准。
- 排班、序列配额、轮班、资源日程、日历任务、数独、N 皇后、图着色、背包、分配、覆盖和装箱模型。
- 路由、项目依赖、容量网络、时间线、制造调度、离散事件、风险情景、质量门禁和运行台账。
#快速开始
///|
test {
let model = @moonbit_constraint.new_solver()
let left = model.add_variable(@moonbit_constraint.variable("left", 1, 9))
let right = model.add_variable(@moonbit_constraint.variable("right", 1, 9))
model.add_constraint(@moonbit_constraint.sum([left, right], 10))
match model.solve() {
Some(solution) => debug_inspect(solution.values, content="[1, 9]")
None => fail("the model should be satisfiable")
}
}#CLI、架构与质量门禁
moon run cmd/main
moon run --target native cmd/benchmark
moon check --target all --deny-warn
moon test --target all --deny-warn
moon fmt
moon info求解流程为“有限域与模型 → 约束传播 → 启发式分支 → 完整赋值校验”；应用模块负责领域模型、结果渲染、校验报告和运行指标。基准原始结果见 BENCHMARKS.md，CI 工作流见 .github/workflows/test.yml。
#许可证
本项目采用 Apache License 2.0。
#AllocationInstance
pub struct AllocationInstance {
items : Array[AllocationItem]
capacities : Array[Int]
group_limits : Array[(Int, Int, Int, Int)]
}A validated collection of item and bin data.
#AllocationInstance::add_group_limit
fn AllocationInstance::add_group_limit(self : AllocationInstance, bin : Int, group : Int, minimum : Int, maximum : Int) -> BoolAdd a lower and upper count for a group on one bin.
#AllocationInstance::bin_count
Return the number of bins.
#AllocationInstance::capacity
Read a bin capacity.
#AllocationInstance::item
Read an item.
#AllocationInstance::item_count
Return the number of items.
#AllocationItem
pub struct AllocationItem {
id : Int
weight : Int
value : Int
group : Int
}Capacity-aware allocation models for packing and resource assignment.
The model separates item data from a mutable candidate plan. This makes it
suitable for greedy seed construction, neighborhood improvement, and
validation before the assignment is handed to a finite-domain Solver.
#AllocationItem::group
Read the group identifier.
#AllocationItem::id
Read the item identifier.
#AllocationItem::value
Read the item value.
#AllocationItem::weight
Read the item weight.
#AllocationPlan
A partial or complete item-to-bin assignment.
#AllocationPlan::assign
Assign an item to a bin without checking capacity.
#AllocationPlan::assigned_bin
Read the assigned bin, or -1 when unassigned.
#AllocationPlan::assignments
Return copied assignments.
#AllocationPlan::csv
Render assignments as comma-separated bin ids.
#AllocationPlan::items_in_bin
Return items in a bin.
#AllocationPlan::unassign
Remove an assignment.
#ArtifactBenchmark
pub struct ArtifactBenchmark {
name : String
work : Int
solved : Bool
signature : Int
}Benchmark artifact aggregation and reproducibility checks.
The core benchmark command emits deterministic solver counters. This layer
packages several runs into a suite, checks repeat consistency, and produces
machine-readable evidence for local acceptance and CI summaries.
#ArtifactBenchmark::describe
Return a stable line.
#ArtifactBenchmark::solved
Return solved state.
#ArtifactBenchmark::work
Return artifact work.
#AssignmentMetrics
pub struct AssignmentMetrics {
count : Int
categories : Int
minimum : Int
maximum : Int
spread : Int
transitions : Int
changes : Int
}Analytics for categorical assignments and roster stability.
These functions operate on integer labels and are intentionally independent
of a particular schedule representation. They can score worker rosters,
machine labels, cluster assignments, or any finite categorical plan.
#AssignmentMetrics::categories
Read category count.
#AssignmentMetrics::changes
Read change count.
#AssignmentMetrics::count
Read assignment count.
#AssignmentMetrics::describe
Return a stable metric line.
#AssignmentMetrics::transitions
Read transition count.
#AssignmentProblem
A small assignment problem with one worker per job and a cost objective.
#AssignmentProblem::assignments
Return all job-to-worker assignments.
#AssignmentProblem::cost
Return the total assignment cost.
#AssignmentProblem::is_valid
Validate an assignment solution.
#AssignmentProblem::render
Return a compact assignment report.
#AssignmentProblem::solve
Solve for minimum total assignment cost.
#AssignmentProblem::stats
Return solver statistics.
#AssignmentProblem::worker
Return assigned worker for a job.
#Assumption
A value decision that can be applied to an existing model without
rebuilding its constraints.
#Assumption::describe
Render a decision in a stable diagnostic form.
#Assumption::value
Return the value required by a decision.
#Assumption::variable
Return the variable referenced by a decision.
#AssumptionResult
pub struct AssumptionResult {
satisfiable : Bool
solution : Solution?
stats : SearchStats
assumptions : AssumptionSet
}The result of a satisfiability probe under temporary decisions.
#AssumptionResult::assumptions
Return the decisions used by the probe.
#AssumptionResult::describe
Render a compact probe result.
#AssumptionResult::satisfiable
Whether the probe found a solution.
#AssumptionResult::solution
Return the first solution found by the probe.
#AssumptionResult::stats
Return search counters for the probe.
#AssumptionSet
An ordered set of decisions used for incremental probes.
#AssumptionSet::add
Add a decision when its variable is not already present.
#AssumptionSet::add_value
Add a variable/value pair when its variable is not already present.
#AssumptionSet::contains
Test whether the set contains a variable/value pair.
#AssumptionSet::describe
Render decisions in insertion order.
#AssumptionSet::get
Find a decision for a variable.
#AssumptionSet::is_empty
Whether the set has no decisions.
#AssumptionSet::length
Number of decisions in the set.
#AssumptionSet::values
Return a copy of the decisions.
#BenchmarkArtifactSuite
A benchmark suite.
#BenchmarkArtifactSuite::add
fn BenchmarkArtifactSuite::add(self : BenchmarkArtifactSuite, benchmark : ArtifactBenchmark) -> BoolAdd a case with a unique name.
#BenchmarkArtifactSuite::all_solved
Return whether all cases are solved.
#BenchmarkArtifactSuite::describe
Return a stable suite report.
#BenchmarkArtifactSuite::heaviest
Return the heaviest case.
#BenchmarkArtifactSuite::length
Return case count.
#BenchmarkArtifactSuite::markdown
Render a suite as markdown.
#BenchmarkArtifactSuite::signature
Return a suite signature.
#BenchmarkArtifactSuite::summary
Return a summary statistic.
#BenchmarkArtifactSuite::total_work
Return total work.
#BenchmarkArtifactSuite::unique_signatures
Return whether signatures are unique.
#BenchmarkArtifactSuite::unsolved
Return unsolved case count.
#BenchmarkArtifactSuite::work_values
Return the work values in case order.
#BenchmarkResult
pub struct BenchmarkResult {
name : String
solved : Bool
solutions : Int
nodes : Int
propagations : Int
checks : Int
pruned : Int
depth : Int
repeat_count : Int
}A deterministic solver benchmark result.
#BenchmarkResult::checks
Return constraint-check count.
#BenchmarkResult::depth
Return maximum depth.
#BenchmarkResult::describe
Return a stable one-line benchmark record.
#BenchmarkResult::name
Return the benchmark name.
#BenchmarkResult::nodes
Return visited node count.
#BenchmarkResult::propagations
Return propagation count.
#BenchmarkResult::pruned
Return pruned-value count.
#BenchmarkResult::repeat_count
Return number of repetitions.
#BenchmarkResult::solutions
Return the number of collected solutions.
#BenchmarkResult::solved
Return whether the case produced the expected satisfiability result.
#BenchmarkResult::work
Return the deterministic work score used when wall-clock noise is high.
#BinPacking
A bin-packing assignment model using one-hot item/bin variables.
#BinPacking::bin_of
Return the selected bin for an item.
#BinPacking::is_valid
Validate a packing solution.
#BinPacking::loads
Return the bin load vector.
#BinPacking::render
Render bin loads and item assignments.
#BinPacking::solve
Solve the packing model.
#BinPacking::stats
Return solver statistics.
#BooleanCircuit
A small Boolean circuit builder that exposes intermediate variables.
#BooleanCircuit::and_gate
Add an AND gate and return its result variable.
#BooleanCircuit::constant
Add a constant Boolean variable.
#BooleanCircuit::input
Add a named input to a circuit.
#BooleanCircuit::not_gate
Add a NOT gate.
#BooleanCircuit::or_gate
Add an OR gate.
#BooleanCircuit::output_values
Return output values from a solution.
#BooleanCircuit::outputs
Return circuit output identifiers.
#BooleanCircuit::solve
Solve the circuit.
#BooleanCircuit::solve_all
Enumerate circuit assignments.
#BooleanCircuit::solver
Return the circuit solver.
#BooleanCircuit::xor_gate
Add an XOR gate.
#CalendarModel
pub struct CalendarModel {
solver : Solver
tasks : Array[CalendarTask]
horizon : Int
resources : Int
}A mutable interval calendar model.
#CalendarModel::add_task
fn CalendarModel::add_task(self : CalendarModel, name : String, duration : Int, resource : Int) -> Int?Add an interval task and return its task id.
#CalendarModel::all_tasks_fit
Check that every task metadata record fits the configured horizon.
#CalendarModel::configure
Configure search.
#CalendarModel::earliest_start
Return the earliest start time in a solution.
#CalendarModel::end_domains
Return the current end domains for every task.
#CalendarModel::end_of
Read a task's end in a solution.
#CalendarModel::fingerprint
Return a stable schedule fingerprint.
#CalendarModel::fix_start
Fix a task at one start time.
#CalendarModel::horizon
Return the planning horizon.
#CalendarModel::idle_time
Return idle horizon time after accounting for total task duration.
#CalendarModel::intervals
fn CalendarModel::intervals(self : CalendarModel, solution : Solution) -> Array[(String, Int, Int, Int)]Return task intervals for a solution.
#CalendarModel::is_valid
Validate a complete calendar solution.
#CalendarModel::latest_end
Return the latest end time in a solution.
#CalendarModel::makespan
Return the latest end time in a solution.
#CalendarModel::post_all_resources_non_overlap
Require every resource to execute non-overlapping tasks.
#CalendarModel::post_allowed_starts
fn CalendarModel::post_allowed_starts(self : CalendarModel, task_id : Int, starts : Array[Int]) -> UnitRestrict all task starts to one of a finite set of slots.
#CalendarModel::post_deadline
Require a task to finish no later than a deadline.
#CalendarModel::post_precedence
fn CalendarModel::post_precedence(self : CalendarModel, first : Int, second : Int, gap : Int) -> UnitRequire the second task to begin after the first task ends.
#CalendarModel::post_resource_capacity
fn CalendarModel::post_resource_capacity(self : CalendarModel, resource : Int, capacity : Int) -> UnitLimit simultaneous demand on one resource group.
#CalendarModel::post_resource_non_overlap
Require tasks on one resource not to overlap.
#CalendarModel::post_start_window
fn CalendarModel::post_start_window(self : CalendarModel, task_id : Int, lower : Int, upper : Int) -> UnitRestrict a task to a start-time window.
#CalendarModel::render
Render tasks by start time and resource id.
#CalendarModel::resource_count
Return the resource count.
#CalendarModel::resource_load
Sum task durations assigned to one resource.
#CalendarModel::resource_task_count
Return the number of tasks assigned to a resource.
#CalendarModel::solve
Solve the calendar.
#CalendarModel::solve_all
Enumerate calendar solutions.
#CalendarModel::start_domains
Return the current start domains for every task.
#CalendarModel::start_of
Read a task's start in a solution.
#CalendarModel::stats
Return the latest statistics.
#CalendarModel::summary
Compute utilization counters from a solution.
#CalendarModel::task
Return a task or abort on an invalid id.
#CalendarModel::task_count
Return the number of tasks.
#CalendarModel::task_ids_for_resource
Return ids assigned to one resource.
#CalendarModel::task_names
Return task names in insertion order.
#CalendarModel::tasks
Return all task metadata.
#CalendarModel::total_duration
Return total duration across all tasks.
#CalendarSummary
A summary of calendar utilization.
#CalendarSummary::describe
Render utilization counters.
#CalendarSummary::makespan
Return the schedule makespan.
#CalendarSummary::occupied
Return total occupied time.
#CalendarSummary::resource_load
Return per-resource occupied time.
#CalendarSummary::tasks
Return the scheduled task count.
#CalendarTask
pub struct CalendarTask {
name : String
start : Int
end : Int
duration : Int
resource : Int
}A calendar template for interval tasks with explicit end variables.
It provides a compact bridge between domain constraints and resource
planning applications.
#CalendarTask::describe
Render task metadata without a solution.
#CalendarTask::duration
Return a task's duration.
#CalendarTask::end
Return a task's end variable.
#CalendarTask::fits
Return whether a task can fit in a horizon.
#CalendarTask::resource
Return a task's resource.
#CalendarTask::start
Return a task's start variable.
#CalendarWindow
pub struct CalendarWindow {
start : Int
end : Int
}Calendar window metrics for service and scheduling reports.
#CalendarWindow::contains
#CalendarWindow::expand
#CalendarWindow::intersection
#CalendarWindow::overlaps
#CalendarWindow::shift
#CalendarWindow::touches
#CapacityArc
pub struct CapacityArc {
from : Int
to : Int
capacity : Int
cost : Int
}Capacity-network models for assignment and transport pipelines.
This layer complements the dense graph algorithms with an explicit network
object that tracks capacities, supplies, and a computed flow assignment.
#CapacityNetwork
A capacity network.
#CapacityNetwork::add_arc
fn CapacityNetwork::add_arc(self : CapacityNetwork, from : Int, to : Int, capacity : Int, cost : Int) -> BoolAdd a directed capacity arc.
#CapacityNetwork::arc_count
Return arc count.
#CapacityNetwork::arcs
Return copied arcs.
#CapacityNetwork::capacity_matrix
Return an integer capacity matrix.
#CapacityNetwork::cut_capacity
Return a min-cut capacity from a reachable partition.
#CapacityNetwork::flow_cost
Return the total cost of a flow matrix.
#CapacityNetwork::max_flow
Compute a maximum flow value and flow matrix.
#CapacityNetwork::outgoing
Return outgoing arcs.
#CapacityNetwork::signature
Return a deterministic network fingerprint.
#CapacityNetwork::validate_flow
Validate a flow matrix against arc capacities.
#CapacityNetwork::vertex_count
Return vertex count.
#ConflictReport
pub struct ConflictReport {
satisfiable : Bool
core : AssumptionSet
probes : Int
original_size : Int
}A deletion-based conflict report for a set of temporary decisions.
#ConflictReport::core
Return the irreducible-by-deletion conflict subset.
#ConflictReport::describe
Render the report for logs or a diagnostics page.
#ConflictReport::original_size
Return the number of decisions initially examined.
#ConflictReport::probes
Return the number of solver probes used to derive the report.
#ConflictReport::satisfiable
Whether the complete decision set is satisfiable.
#Constraint
pub enum Constraint {
Equal(Int, Int)
NotEqual(Int, Int)
LessThan(Int, Int)
LessEqual(Int, Int)
GreaterThan(Int, Int)
GreaterEqual(Int, Int)
AllDifferent(Array[Int])
Sum(Array[Int], Int)
Element(Int, Array[Int], Int)
Linear(Array[(Int, Int)], Int)
LinearLessEqual(Array[(Int, Int)], Int)
LinearGreaterEqual(Array[(Int, Int)], Int)
Between(Int, Int, Int)
Member(Int, Array[Int])
CountValue(Array[Int], Int, Int)
AtMostValue(Array[Int], Int, Int)
AtLeastValue(Array[Int], Int, Int)
Minimum(Array[Int], Int)
Maximum(Array[Int], Int)
Absolute(Int, Int)
Distance(Int, Int, Int)
NotDistance(Int, Int, Int)
Table(Array[Int], Array[Array[Int]])
NoOverlap(Array[(Int, Int)])
Cumulative(Array[(Int, Int, Int)], Int)
} derive(Debug)Constraints supported by the first solver engine.
#ConstraintBuilder
Higher-level constraint model builders.
The builder layer packages recurring finite-domain patterns—grids,
cardinality rules, automaton-like transitions, and resource windows—into
small APIs that remain inspectable through the underlying Solver.
#ConstraintBuilder::add_bool
Add a Boolean variable.
#ConstraintBuilder::add_int
fn ConstraintBuilder::add_int(self : ConstraintBuilder, name : String, lower : Int, upper : Int) -> IntAdd a bounded integer variable.
#ConstraintBuilder::adjacent_different
Add adjacent inequality constraints.
#ConstraintBuilder::all_different
Add all-different constraints for a variable list.
#ConstraintBuilder::at_least_count
fn ConstraintBuilder::at_least_count(self : ConstraintBuilder, variables : Array[Int], value : Int, count : Int) -> BoolAdd a lower count.
#ConstraintBuilder::at_most_count
fn ConstraintBuilder::at_most_count(self : ConstraintBuilder, variables : Array[Int], value : Int, count : Int) -> BoolAdd an upper count.
#ConstraintBuilder::cumulative
fn ConstraintBuilder::cumulative(self : ConstraintBuilder, starts : Array[Int], durations : Array[Int], heights : Array[Int], capacity : Int) -> BoolAdd a cumulative capacity constraint.
#ConstraintBuilder::describe
Return a builder summary.
#ConstraintBuilder::exact_count
fn ConstraintBuilder::exact_count(self : ConstraintBuilder, variables : Array[Int], value : Int, count : Int) -> BoolAdd an exact count of a value.
#ConstraintBuilder::exact_sum
fn ConstraintBuilder::exact_sum(self : ConstraintBuilder, variables : Array[Int], target : Int) -> BoolAdd a fixed target sum.
#ConstraintBuilder::fix
Fix a variable.
#ConstraintBuilder::grid
fn ConstraintBuilder::grid(self : ConstraintBuilder, name : String, rows : Int, columns : Int, lower : Int, upper : Int) -> Array[Int]Add a row-major integer grid with one variable per cell.
#ConstraintBuilder::grid_columns
fn ConstraintBuilder::grid_columns(self : ConstraintBuilder, grid : Array[Int], rows : Int, columns : Int) -> BoolAdd all-different rules to each grid column.
#ConstraintBuilder::grid_rows
fn ConstraintBuilder::grid_rows(self : ConstraintBuilder, grid : Array[Int], rows : Int, columns : Int) -> BoolAdd all-different rules to each grid row.
#ConstraintBuilder::minimum_distance
fn ConstraintBuilder::minimum_distance(self : ConstraintBuilder, variables : Array[Int], distance : Int) -> BoolAdd pairwise separation by a minimum absolute distance.
#ConstraintBuilder::no_overlap
fn ConstraintBuilder::no_overlap(self : ConstraintBuilder, starts : Array[Int], durations : Array[Int]) -> BoolAdd interval no-overlap constraints.
#ConstraintBuilder::nondecreasing_chain
Add a nondecreasing chain.
#ConstraintBuilder::signature
Return a model fingerprint.
#ConstraintBuilder::solve
Solve once.
#ConstraintBuilder::solve_all
Enumerate a bounded number of solutions.
#ConstraintBuilder::solver
Return the underlying solver.
#ConstraintBuilder::strict_chain
Add a chain of strict ordering constraints.
#ConstraintBuilder::transition_table
fn ConstraintBuilder::transition_table(self : ConstraintBuilder, variables : Array[Int], transitions : Array[Array[Int]]) -> BoolAdd a transition table to adjacent state variables.
#ConstraintBuilder::variables
Return all variable ids.
#ConstraintBuilder::weighted_at_least
fn ConstraintBuilder::weighted_at_least(self : ConstraintBuilder, terms : Array[(Int, Int)], target : Int) -> BoolAdd a weighted lower bound.
#ConstraintBuilder::weighted_at_most
fn ConstraintBuilder::weighted_at_most(self : ConstraintBuilder, terms : Array[(Int, Int)], target : Int) -> BoolAdd a weighted sum bound.
#ConstraintInfo
pub struct ConstraintInfo {
kind : ConstraintKind
arity : Int
variables : Array[Int]
label : String
}Counted information about one constraint for diagnostics.
#ConstraintInfo::arity
Read a diagnostic record's arity.
#ConstraintInfo::describe
Render a constraint diagnostic in one line.
#ConstraintInfo::kind
Read a diagnostic record's kind.
#ConstraintInfo::label
Read a diagnostic record's label.
#ConstraintInfo::variables
Read a diagnostic record's variables.
#ConstraintKind
Classification of the supported constraint families.
#ConstraintRecipe
A reusable recipe object.
#ConstraintRecipe::builder
#ConstraintRecipe::describe
Return a recipe model summary.
#ConstraintRecipe::post_adjacent_change
#ConstraintRecipe::post_all_different
Post a row of all-different variables.
#ConstraintRecipe::post_at_least
fn ConstraintRecipe::post_at_least(self : ConstraintRecipe, variables : Array[Int], value : Int, count : Int) -> Bool#ConstraintRecipe::post_at_most
fn ConstraintRecipe::post_at_most(self : ConstraintRecipe, variables : Array[Int], value : Int, count : Int) -> Bool#ConstraintRecipe::post_chain
#ConstraintRecipe::post_cumulative
fn ConstraintRecipe::post_cumulative(self : ConstraintRecipe, starts : Array[Int], durations : Array[Int], heights : Array[Int], capacity : Int) -> Bool#ConstraintRecipe::post_exact_count
fn ConstraintRecipe::post_exact_count(self : ConstraintRecipe, variables : Array[Int], value : Int, count : Int) -> Bool#ConstraintRecipe::post_exact_sum
fn ConstraintRecipe::post_exact_sum(self : ConstraintRecipe, variables : Array[Int], target : Int) -> Bool#ConstraintRecipe::post_fix
Post a fixed assignment.
#ConstraintRecipe::post_grid_rules
fn ConstraintRecipe::post_grid_rules(self : ConstraintRecipe, grid : Array[Int], rows : Int, columns : Int) -> BoolPost a grid's row and column rules.
#ConstraintRecipe::post_lower
fn ConstraintRecipe::post_lower(self : ConstraintRecipe, terms : Array[(Int, Int)], target : Int) -> Bool#ConstraintRecipe::post_minimum_distance
fn ConstraintRecipe::post_minimum_distance(self : ConstraintRecipe, variables : Array[Int], distance : Int) -> Bool#ConstraintRecipe::post_no_overlap
fn ConstraintRecipe::post_no_overlap(self : ConstraintRecipe, starts : Array[Int], durations : Array[Int]) -> Bool#ConstraintRecipe::post_non_decreasing
#ConstraintRecipe::post_transition
fn ConstraintRecipe::post_transition(self : ConstraintRecipe, variables : Array[Int], transitions : Array[Array[Int]]) -> Bool#ConstraintRecipe::post_upper
fn ConstraintRecipe::post_upper(self : ConstraintRecipe, terms : Array[(Int, Int)], target : Int) -> BoolPost bounded weighted rules.
#ConstraintRecipe::recipe_binary_vector
Build a binary selection vector.
#ConstraintRecipe::recipe_one_hot
#ConstraintRecipe::solve
Solve a recipe.
#ConstraintRecipe::solve_all
#ConstraintRecipe::valid
Return whether all posted recipes succeeded.
#ConstraintSet
A reusable batch of constraints that can be assembled before posting.
#ConstraintSet::add
Append one constraint.
#ConstraintSet::append
Append every constraint from another set.
#ConstraintSet::constraints
Return a defensive copy.
#ConstraintSet::labels
Render labels for a constraint batch.
#ConstraintSet::length
Number of constraints in the set.
#ConstraintSet::post
Post every constraint and return the posted count.
#ConstraintSet::report
Summarize a constraint set before posting.
#ConstraintSetReport
A small report about a posted constraint batch.
#ConstraintSetReport::count
Return the report's count.
#ConstraintSetReport::describe
Render a batch report.
#ConstraintSetReport::fingerprint
Return a report fingerprint.
#ConstraintSetReport::labels
Return report labels.
#DecisionStep
pub struct DecisionStep {
variable : Int
value : Int
accepted : Bool
depth : Int
}One assignment step recorded by an incremental solving client.
#DecisionStep::accepted
Whether this decision remained consistent.
#DecisionStep::depth
Read the search depth of a decision.
#DecisionStep::describe
Render a decision record.
#DecisionStep::value
Read the value in a decision record.
#DecisionStep::variable
Read the variable in a decision record.
#DecisionTrace
A reusable trace assembled by a model-building client.
#DecisionTrace::accepted_count
Count accepted steps.
#DecisionTrace::describe
Render a multiline trace.
#DecisionTrace::length
Return the number of recorded steps.
#DecisionTrace::maximum_depth
Return the deepest recorded decision.
#DecisionTrace::record
Append a step to a trace.
#DecisionTrace::rejected_count
Count rejected steps.
#DecisionTrace::steps
Return a copy of the trace steps.
#Domain
A finite integer domain represented by an inclusive interval and holes.
#Domain::assign
Narrow the domain to one value.
#Domain::assign_largest
Return a domain after assigning the last available value.
#Domain::assign_smallest
Return a domain after assigning the first available value.
#Domain::choose_largest
Return the largest available value without allocating the full list.
#Domain::choose_smallest
Return the smallest available value without allocating the full list.
#Domain::compact
Render a domain as a compact union of intervals.
#Domain::contains
Test whether value is still available.
#Domain::contains_all
Return whether every value in other also occurs in self.
#Domain::count_range
Number of values in the inclusive range that remain available.
#Domain::describe
Return a stable textual form useful in diagnostics and benchmark output.
#Domain::domain_scale
Multiply every value by a non-zero coefficient.
#Domain::domain_shift
Translate every value by a constant.
#Domain::intersect_range
Keep only values in the inclusive range.
#Domain::intersect_values
Keep only values that also occur in allowed.
#Domain::interval_lower
Return the fixed lower endpoint of the original interval.
#Domain::interval_upper
Return the fixed upper endpoint of the original interval.
#Domain::intervals
Return maximal contiguous segments of the remaining domain.
#Domain::is_contiguous
Whether every value in the enclosing interval is still available.
#Domain::is_empty
Whether no value remains in the domain.
#Domain::is_singleton
Check whether the domain has exactly one value.
#Domain::max
Return the largest value in the domain, or None when it is empty.
#Domain::min
Return the smallest value in the domain, or None when it is empty.
#Domain::next
Return the first available value at or above value.
#Domain::nth
Return the value at a zero-based rank.
#Domain::previous
Return the last available value at or below value.
#Domain::profile
Build a domain profile without exposing implementation fields.
#Domain::rank
Return the zero-based rank of a value, if present.
#Domain::remove
Remove one value. Returns whether the domain changed.
#Domain::remove_above
Remove every value above upper and report whether the domain changed.
#Domain::remove_below
Remove every value below lower and report whether the domain changed.
#Domain::remove_many
Return a domain after removing a sorted list of values.
#Domain::removed_count
Return the number of removed values in the enclosing interval.
#Domain::restore
Restore the removed-value state from another domain with the same interval.
This is used by incremental solver probes to make temporary assumptions
fully reversible after propagation and search.
#Domain::singleton
Return the only value when this domain is a singleton.
#Domain::sum_values
Sum all available values using an overflow-conscious accumulator.
#DomainCatalog
A named domain catalog.
#DomainCatalog::add
fn DomainCatalog::add(self : DomainCatalog, name : String, value_domain : Domain, tag : String) -> Int?Add a unique entry.
#DomainCatalog::available_values
Return the number of available values.
#DomainCatalog::cartesian_size
Return the total Cartesian size capped at a limit.
#DomainCatalog::clone
Return a catalog of cloned domains.
#DomainCatalog::consistent
Return whether every domain is non-empty.
#DomainCatalog::describe
Return a compact catalog description.
#DomainCatalog::empty_entries
Return empty-domain entries.
#DomainCatalog::entries
Return copied entries.
#DomainCatalog::entry
Read an entry.
#DomainCatalog::find
Find an entry by name.
#DomainCatalog::hole_count
Return the number of holes across all domains.
#DomainCatalog::intersect_range
Restrict one domain to an interval.
#DomainCatalog::intersect_values
Restrict one domain to an explicit set.
#DomainCatalog::length
Return catalog length.
#DomainCatalog::most_constrained
Return domains with at most a threshold number of values.
#DomainCatalog::names
Return all names.
#DomainCatalog::remove
Remove a value from one catalog entry.
#DomainCatalog::restrict_all
Return a copy with every domain intersected by a shared interval.
#DomainCatalog::signature
Return a catalog fingerprint.
#DomainCatalog::singleton_entries
Return singleton entries.
#DomainCatalog::sizes
Return all domain sizes.
#DomainCatalog::tagged_bounds
Return the smallest enclosing interval for tagged domains.
#DomainCatalog::tagged_values
Return the union of all values in tagged entries.
#DomainCatalog::with_tag
Return entries with a tag.
#DomainChange
Explain finite-domain changes in a solver-friendly form.
Explanations are deliberately data-only: callers can render them as CLI
diagnostics, attach them to an assumption conflict, or snapshot them in a
regression test without depending on mutable solver internals.
#DomainChange::added_values
Return added values.
#DomainChange::changed
Return whether a domain changed.
#DomainChange::describe
Return a stable explanation.
#DomainChange::removed_values
Return removed values.
#DomainDelta
Return a domain delta between two saved states.
#DomainDelta::after
Read the narrowed domain.
#DomainDelta::before
Read the previous domain.
#DomainDelta::describe
Render a delta for propagation diagnostics.
#DomainDelta::variable
Read the variable from a domain delta.
#DomainEntry
Domain catalogs for application-level variable schemas.
A catalog gives names, domains, tags, and simple filtering operations to
data-driven model builders. It preserves the finite-domain core while
allowing CLI and configuration layers to inspect variables without
depending on solver internals.
#DomainInterval
A maximal contiguous segment of an integer domain.
#DomainInterval::contains
Whether a value is contained in an interval.
#DomainInterval::describe
Return a stable textual interval.
#DomainInterval::intersect
Return the intersection of two intervals, when it exists.
#DomainInterval::lower
Read an interval's lower bound.
#DomainInterval::size
Number of integers in an interval.
#DomainInterval::touches
Whether two intervals overlap or touch.
#DomainInterval::upper
Read an interval's upper bound.
#DomainProfile
pub struct DomainProfile {
minimum : Int?
maximum : Int?
size : Int
interval_count : Int
removed_count : Int
}Return a summary of domain shape for instrumentation.
#DomainProfile::describe
Render a domain profile for metrics output.
#DomainProfile::interval_count
Read the profile's interval count.
#DomainProfile::size
Read the profile's candidate count.
#EventQueue
A sorted event queue.
#EventQueue::peek
Peek at the earliest event.
#EventQueue::pop
Pop the earliest event.
#EventQueue::push
Add an event in stable order.
#EvidenceBundle
A collection of evidence records.
#EvidenceBundle::add
#EvidenceBundle::exact
fn EvidenceBundle::exact(self : EvidenceBundle, name : String, metric : String, value : Int, expected : Int) -> UnitAdd an exact evidence gate.
#EvidenceBundle::maximum
fn EvidenceBundle::maximum(self : EvidenceBundle, name : String, metric : String, value : Int, expected : Int) -> UnitAdd a maximum evidence gate.
#EvidenceBundle::minimum
fn EvidenceBundle::minimum(self : EvidenceBundle, name : String, metric : String, value : Int, expected : Int) -> UnitAdd a minimum evidence gate.
#EvidenceRecord
pub struct EvidenceRecord {
name : String
metric : String
value : Int
expected : Int
passed : Bool
}Compact evidence records for local acceptance runs.
#FiniteRelation
A finite binary relation.
#FiniteRelation::add
Add a pair.
#FiniteRelation::contains
Return whether a pair is present.
#FiniteRelation::functional
Return whether the relation is functional.
#FiniteRelation::image
Return right values supported by a left value.
#FiniteRelation::inverse
Return the inverse relation.
#FiniteRelation::length
Return pair count.
#FiniteRelation::pairs
Return pairs.
#FiniteRelation::preimage
Return left values that support a right value.
#FiniteRelation::restrict
fn FiniteRelation::restrict(self : FiniteRelation, left : IntegerSet, right : IntegerSet) -> FiniteRelationRestrict a relation to left and right sets.
#FiniteRelation::signature
Return a stable relation fingerprint.
#FiniteRelation::table
Return a table of supported pairs.
#FiniteRelation::total
Return whether every left value has a support.
#GraphColoring
An undirected graph-coloring model.
#GraphColoring::adjacency
Return an adjacency matrix, useful for debugging imported graphs.
#GraphColoring::color_of
Return the variable identifier for a vertex.
#GraphColoring::colors
Return the color vector in vertex order.
#GraphColoring::is_valid
Validate a complete coloring.
#GraphColoring::render
Render a solution as vertex:color pairs.
#GraphColoring::solve
Solve a coloring problem once.
#GraphColoring::solve_all
Enumerate up to limit colorings.
#GraphColoring::stats
Return solver statistics.
#GraphEdge
pub struct GraphEdge {
from : Int
to : Int
weight : Int
}Deterministic graph algorithms for constraint-model preprocessing.
Graphs use a dense integer matrix because finite-domain applications often
already have a bounded set of resources, machines, or locations. A value
of -1 means that an edge is absent; zero-weight edges are supported.
#GuardedSolve
pub struct GuardedSolve {
status : SolveStatus
solution : Solution?
stats : SearchStats
reason : String?
}Result of a solve subject to structural and search limits.
#GuardedSolve::describe
Render a guarded solve result.
#GuardedSolve::is_success
Return whether a guarded solve reached a valid solution.
#GuardedSolve::reason
Return an optional rejection or budget reason.
#GuardedSolve::solution
Return the optional solution.
#GuardedSolve::solution_values
Return a defensive copy of a guarded result's solution values.
#GuardedSolve::stats
Return solve counters.
#GuardedSolve::status
Return the solve status.
#GuardedSolve::was_budget_limited
Return whether a guarded solve exhausted its node budget.
#GuardedSolve::was_rejected
Return whether a guarded solve was rejected before search.
#HeuristicResult
A comparative result for one search configuration.
#HeuristicResult::describe
Render a comparative result.
#HeuristicResult::name
Read the strategy name.
#HeuristicResult::solutions
Read solution count.
#HeuristicResult::stats
Read search statistics.
#IntMatrix
Integer matrix utilities used by grid constraints and resource heatmaps.
Matrices are row-major, bounds checked at construction, and intentionally
use integers so they can be shared by solver preprocessing, image-like
occupancy grids, and deterministic benchmark fixtures.
#IntMatrix::add
Add two matrices.
#IntMatrix::box_blur
Return a centered moving average with integer division.
#IntMatrix::clamp
Clamp every cell into an inclusive range.
#IntMatrix::column
Return a copied column.
#IntMatrix::column_count
Return column count.
#IntMatrix::column_sums
Return column sums.
#IntMatrix::component_size
Count a connected component of equal-valued cells.
#IntMatrix::convolve3x3
Apply a four-neighbor convolution kernel.
#IntMatrix::count
Count cells equal to a value.
#IntMatrix::csv
Render rows as comma-separated integers.
#IntMatrix::diagonal
Return the main diagonal.
#IntMatrix::get
Read a cell.
#IntMatrix::grid_path
Return a four-neighbor shortest path on cells equal to a passable value.
#IntMatrix::maximum
Return the largest cell value, or None for an empty matrix.
#IntMatrix::minimum
Return the smallest cell value, or None for an empty matrix.
#IntMatrix::multiply
Multiply two matrices.
#IntMatrix::neighbors4
Return the four-neighbor cells of a coordinate.
#IntMatrix::prefix_sum
Compute an integral prefix-sum matrix.
#IntMatrix::reflect_horizontal
Reflect across the vertical axis.
#IntMatrix::reflect_vertical
Reflect across the horizontal axis.
#IntMatrix::render
Render a matrix with spaces between cells.
#IntMatrix::rotate_clockwise
Rotate clockwise by 90 degrees.
#IntMatrix::row
Return a copied row.
#IntMatrix::scale
Multiply each cell by a scalar.
#IntMatrix::set
Write a cell and return whether it changed.
#IntMatrix::sign
Replace each cell by its sign.
#IntMatrix::signature
Return a stable matrix fingerprint.
#IntMatrix::subtract
Subtract two matrices.
#IntMatrix::threshold
Return a binary occupancy mask.
#IntMatrix::trace
Return the trace of a square matrix.
#IntMatrix::transpose
Return the transpose.
#IntMatrix::valid_cell
Validate a cell coordinate.
#IntegerHistogram
A histogram with fixed-width integer buckets.
#IntegerHistogram::add
Add a value to a histogram when it falls within its buckets.
#IntegerHistogram::add_all
Add all values and return the number accepted.
#IntegerHistogram::bucket
Return a bucket count.
#IntegerHistogram::counts
Return bucket counts.
#IntegerHistogram::peak
Return the bucket with the largest count.
#IntegerHistogram::total
Return total accepted values.
#IntegerObjective
Integer optimization utilities for candidate plans.
#IntegerObjective::compare
Compare vectors lexicographically.
#IntegerObjective::dominates
Return whether this vector dominates another.
#IntegerObjective::length
Return objective dimension.
#IntegerObjective::values
Return copied objective values.
#IntegerRegression
pub struct IntegerRegression {
slope_scaled : Int
intercept : Int
error : Int
}A pair of samples for regression.
#IntegerRegression::error
Read squared error.
#IntegerRegression::intercept
Read intercept.
#IntegerRegression::slope_scaled
Read scaled slope.
#IntegerSet
Small finite-set and relation structures for domain preprocessing.
IntegerSet keeps sorted unique values and offers the operations commonly
needed when filtering domains, composing allowed-value tables, or
explaining the support of a constraint.
#IntegerSet::contains
Return whether a value belongs to the set.
#IntegerSet::contiguous
Return whether all values form a contiguous interval.
#IntegerSet::describe
Return a stable set representation.
#IntegerSet::difference
Return values in the left set but not the right set.
#IntegerSet::insert
Insert a value.
#IntegerSet::intersection
Return the intersection.
#IntegerSet::maximum
Return the largest value.
#IntegerSet::minimum
Return the smallest value.
#IntegerSet::remove
Remove a value.
#IntegerSet::runs
Return contiguous runs as inclusive pairs.
#IntegerSet::subset_of
Return whether every value is in another set.
#IntegerSet::union
Return the union.
#IntegerSet::values
Return copied sorted values.
#IntegerSummary
pub struct IntegerSummary {
count : Int
total : Int
minimum : Int
maximum : Int
median : Int
mean : Int
}Integer statistics for deterministic solver and application telemetry.
Metrics avoid floating-point dependence so benchmark snapshots remain
reproducible across native, wasm, and wasm-gc targets. Percentage and
correlation helpers use explicit scale factors documented by their names.
#IntegerSummary::count
Read sample count.
#IntegerSummary::describe
Return a stable summary line.
#IntervalTask
pub struct IntervalTask {
name : String
start : Int
duration : Int
demand : Int
latest_start : Int
}A finite-horizon interval scheduling model.
#Knapsack
A knapsack model and its objective variable.
#Knapsack::is_valid
Return whether a solution is capacity-feasible.
#Knapsack::objective_variable
Return the objective variable.
#Knapsack::render
Render a solution as a compact item list.
#Knapsack::selected_names
Return all selected item names.
#Knapsack::selection_variable
Return the item selection variable.
#Knapsack::solve
Solve for a maximum-value selection.
#Knapsack::value
Return the total value of a solution.
#KnapsackItem
pub struct KnapsackItem {
name : String
weight : Int
value : Int
selected : Int
}A 0/1 knapsack model with a real weighted capacity constraint.
#LatinPuzzle
A Latin square model.
#LatinPuzzle::candidates
Return legal values for a Latin square cell.
#LatinPuzzle::set
Set a Latin square cell.
#LatinPuzzle::signature
Return a puzzle fingerprint.
#LatinPuzzle::solved
Return whether a Latin square is complete and valid.
#LatinSquare
A Latin square model with row and column permutation constraints.
#LatinSquare::cell
Return a cell identifier.
#LatinSquare::fix_first_row
Fix the first row to canonical symbol order.
#LatinSquare::is_valid
Validate a Latin square solution.
#LatinSquare::matrix
Return a matrix of a Latin square solution.
#LatinSquare::solve
Solve the Latin square once.
#LatinSquare::stats
Return search statistics.
#LinearBuilder
A small fluent builder for weighted linear constraints.
#LinearBuilder::constant
Return the constant offset.
#LinearBuilder::equals
Convert an expression equality into a solver constraint.
#LinearBuilder::offset
Add a constant offset to the expression.
#LinearBuilder::post_equals
Add an expression equality to a named model.
#LinearBuilder::term
Append coefficient * variable to an expression.
#LinearBuilder::terms
Return a defensive copy of expression terms.
#MachineSpec
pub struct MachineSpec {
id : Int
name : String
capacity : Int
}Job-shop and machine-sequencing utilities.
Manufacturing operations are represented as fixed machine assignments with
setup and processing durations. The module provides deterministic dispatch
rules and validates the resulting Gantt plan before it is used as a solver
seed or exported to a shop-floor integration.
#MachineSpec::capacity
Read machine capacity.
#MagicSquare
A magic-square model with all-different cells and line sums.
#MagicSquare::cell
Return a cell identifier.
#MagicSquare::is_valid
Return whether a magic-square assignment is valid.
#MagicSquare::matrix
Return a matrix from a magic-square solution.
#MagicSquare::solve
Solve a magic square once.
#MagicSquare::stats
Return statistics from the most recent solve.
#MagicSquare::target
Return the target line sum.
#ManufacturingInstance
pub struct ManufacturingInstance {
machines : Array[MachineSpec]
operations : Array[ManufacturingOperation]
setup_matrix : Array[Array[Int]]
}A validated manufacturing instance.
#ManufacturingInstance::machine
Read a machine.
#ManufacturingInstance::machine_count
Return machine count.
#ManufacturingInstance::operation
fn ManufacturingInstance::operation(self : ManufacturingInstance, id : Int) -> ManufacturingOperationRead an operation.
#ManufacturingInstance::operation_count
Return operation count.
#ManufacturingOperation
pub struct ManufacturingOperation {
id : Int
job : Int
sequence : Int
machine : Int
setup : Int
duration : Int
family : Int
due : Int
}A fixed-machine operation.
#ManufacturingOperation::job
Read operation job.
#ManufacturingOperation::machine
Read assigned machine.
#ManufacturingOperation::sequence
Read operation sequence.
#ManufacturingOperation::total_duration
Return setup plus processing duration.
#ManufacturingSchedule
A concrete operation start schedule.
#ManufacturingSchedule::machine_sequence
fn ManufacturingSchedule::machine_sequence(self : ManufacturingSchedule, machine : Int) -> Array[Int]Return machine sequence.
#ManufacturingSchedule::queue
fn ManufacturingSchedule::queue(self : ManufacturingSchedule, machine : Int, operation : Int) -> BoolAdd an operation to a machine sequence.
#ManufacturingSchedule::set_start
fn ManufacturingSchedule::set_start(self : ManufacturingSchedule, operation : Int, start : Int) -> BoolSet an operation start time.
#ManufacturingSchedule::start
Read an operation start.
#ManufacturingSchedule::starts
Return a copied start array.
#ModelBuilder
A named facade over Solver for applications that prefer model-building
APIs over manually managing integer identifiers.
#ModelBuilder::configure
Configure the named model's search strategy.
#ModelBuilder::constraint_count
Return the number of constraints currently posted.
#ModelBuilder::describe_solution
Format a solution using the model's declared variable order.
#ModelBuilder::find
Resolve a variable name to an identifier.
#ModelBuilder::finite
Add a named variable with an explicit finite domain.
#ModelBuilder::fix
Fix a named variable to one value.
#ModelBuilder::int
Add a bounded integer variable and return its identifier.
#ModelBuilder::limit
Set the solution limit for the named model.
#ModelBuilder::named_solution
Bind a solution to a name/value map.
#ModelBuilder::names
Return a copy of the name-to-id map for diagnostics and adapters.
#ModelBuilder::post
Post a constraint to the underlying solver.
#ModelBuilder::post_all_different
Add an all-different constraint by variable names.
#ModelBuilder::post_count
fn ModelBuilder::post_count(self : ModelBuilder, names : Array[String], value : Int, count : Int) -> UnitAdd an exact count constraint by variable names.
#ModelBuilder::post_equal
A readable, checked model construction helper for equality.
#ModelBuilder::post_less_than
A readable, checked model construction helper for ordering.
#ModelBuilder::post_not_equal
A readable, checked model construction helper for inequality.
#ModelBuilder::require
Resolve a name or abort with a model-building error.
#ModelBuilder::solve
Solve the named model once.
#ModelBuilder::solve_all
Enumerate solutions of the named model.
#ModelBuilder::solver
Access the underlying solver for advanced constraints or diagnostics.
#ModelBuilder::summary
Return a summary that keeps names in insertion order.
#ModelBuilder::variable_count
Return the number of variables currently declared.
#ModelLimits
pub struct ModelLimits {
max_variables : Int
max_constraints : Int
max_domain_size : Int
}Model-size limits for services that accept user-authored constraints.
#ModelReport
pub struct ModelReport {
name : String
variables : Int
constraints : Int
solved : Bool
solutions : Int
nodes : Int
propagations : Int
checks : Int
pruned : Int
signature : Int
}Structured model and solve reports for CLI and CI artifacts.
Reports keep metrics close to the model that produced them, making it
possible to compare revisions without scraping human-oriented logs.
#ModelReport::constraints
Return constraint count.
#ModelReport::describe
Return a stable one-line report.
#ModelReport::solutions
Return solution count.
#ModelReport::variables
Return variable count.
#ModelSchema
A model schema.
#ModelSchema::add
Add a field if its id and name are unique.
#ModelSchema::cartesian_size
Return the Cartesian size capped at a limit.
#ModelSchema::changed_defaults
Return an assignment difference from defaults.
#ModelSchema::defaults
Return default values.
#ModelSchema::describe
Return a schema summary.
#ModelSchema::field
Read a field.
#ModelSchema::find
Find a field by name.
#ModelSchema::length
Return schema field count.
#ModelSchema::names
Return field names.
#ModelSchema::report
Return a schema report.
#ModelSchema::signature
Return a stable schema signature.
#ModelSchema::validate
Validate values against the schema.
#ModelSnapshot
pub struct ModelSnapshot {
domains : Array[Domain]
max_solutions : Int
search_config : SearchConfig
}A reversible copy of the variable domains and search settings.
#ModelSnapshot::describe
Render the saved domain state.
#ModelSnapshot::domain_of
Return a copy of a saved domain.
#ModelSnapshot::variable_count
Return the number of variables in a snapshot.
#NQueens
A non-attacking N-Queens model.
#NQueens::compact
Return a compact row-to-column representation.
#NQueens::is_valid
Validate a complete N-Queens placement.
#NQueens::queen_ids
Return queen variables in row order.
#NQueens::render
Draw a placement with Q and . characters.
#NQueens::solve_all
Enumerate up to limit placements.
#NonogramLine
Small exact puzzle models backed by finite-domain primitives.
These helpers are useful as regression fixtures as well as educational
examples: a puzzle carries its dimensions and clues, generates legal line
patterns, propagates forced cells, and exposes a stable verification path.
#NonogramLine::matches
Return whether a binary line satisfies this clue.
#NonogramLine::minimum_used
Return the minimum cells required by clues and separators.
#NonogramLine::patterns
Generate all legal binary patterns for a clue.
#NonogramPuzzle
pub struct NonogramPuzzle {
rows : Array[NonogramLine]
columns : Array[NonogramLine]
cells : IntMatrix
}A rectangular nonogram puzzle.
#NonogramPuzzle::cell
Read a cell, or -1 when outside the puzzle.
#NonogramPuzzle::column_count
Read column count.
#NonogramPuzzle::consistent
Return whether the current partial board can still satisfy clues.
#NonogramPuzzle::fix
Fix a cell to empty or filled.
#NonogramPuzzle::propagate
Propagate forced cells from row and column pattern intersections.
#NonogramPuzzle::render
Render a board with ? for unknown cells.
#NonogramPuzzle::row_count
Read puzzle dimensions.
#NonogramPuzzle::solved
Return whether every row and column is solved.
#Objective
A multi-component objective used by application adapters.
#Objective::best
Return the preferred solution from a non-empty array.
#Objective::describe
Return a simple objective summary.
#Objective::dominates
Return whether a solution dominates another over all objective terms.
#Objective::is_better
Return whether candidate is preferred to incumbent lexicographically.
#Objective::pareto_front
Filter a solution set to its Pareto frontier.
#Objective::score
Calculate a weighted scalar score.
#Objective::terms
Return objective terms.
#Objective::then
fn Objective::then(self : Objective, variable : Int, direction : OptimizationDirection, weight : Int) -> ObjectiveAppend a lower-priority objective component.
#ObjectiveTerm
One lexicographic objective component.
#ObjectiveTerm::direction
Read an objective direction.
#ObjectiveTerm::variable
Read an objective variable identifier.
#ObjectiveTerm::weight
Read the objective weight.
#OperationalLedger
#OperationalLedger::append
#OperationalLedger::attempt_sum
#OperationalLedger::contains
#OperationalLedger::count_failure
#OperationalLedger::count_retryable
#OperationalLedger::count_state
#OperationalLedger::count_success
#OperationalLedger::count_terminal
#OperationalLedger::duration_sum
#OperationalLedger::error_codes
#OperationalLedger::failure_rate
#OperationalLedger::filter_state
fn OperationalLedger::filter_state(self : OperationalLedger, state : OperationalState) -> Array[OperationalRecord]#OperationalLedger::get
#OperationalLedger::index_of
#OperationalLedger::max_duration
#OperationalLedger::min_duration
#OperationalLedger::output_sum
#OperationalLedger::remove
#OperationalLedger::success_rate
#OperationalLedger::summary
#OperationalLedger::throughput
#OperationalLedger::upsert
#OperationalRecord
pub struct OperationalRecord {
key : String
state : OperationalState
started_at : Int
finished_at : Int
attempts : Int
output_count : Int
error_code : Int
}A compact, serializable record for one operational job.
#OperationalState
pub enum OperationalState {
LedgerQueued
LedgerRunning
LedgerSucceeded
LedgerFailed
LedgerSkipped
}Lifecycle states used by long-running planning and validation jobs.
#OperationalSummary
pub struct OperationalSummary {
total : Int
queued : Int
running : Int
succeeded : Int
failed : Int
skipped : Int
duration : Int
outputs : Int
attempts : Int
success_rate : Int
failure_rate : Int
throughput : Int
}#OptimizationDirection
Direction used by optimization helpers.
#OptimizationResult
A result that includes both a solution and its measured objective value.
#OptimizationResult::objective
Return the objective value.
#OptimizationResult::solution
Return the optimized solution.
#PlanningCapacityPoint
pub struct PlanningCapacityPoint {
resource : Int
time : Int
load : Int
capacity : Int
}Planning utilities that bridge schedules, routes, and resource calendars.
These functions answer operational questions around a model—what is late,
where is capacity tight, and which task should be moved—without changing
the underlying solver representation.
#PlanningCapacityPoint::overloaded
Return whether overloaded.
#PlanningCapacityPoint::residual
Return residual capacity.
#PlanningDependency
pub struct PlanningDependency {
before : Int
after : Int
lag : Int
}A precedence arc from one task to another.
#PlanningDependency::after
Read the successor.
#PlanningDependency::before
Read the predecessor.
#PlanningDependency::lag
Read the minimum lag.
#PlanningTask
pub struct PlanningTask {
id : Int
name : String
duration : Int
resource : Int
demand : Int
release : Int
due : Int
}Project planning primitives for dependency-constrained work.
A planning task has a start variable, a fixed duration, an optional
release time, a due date, and a resource demand. The API supports both a
fast critical-path analysis and a Solver-backed schedule for exact checks.
#PlanningTask::contains
Return whether an interval contains a time point.
#PlanningTask::demand
Read the resource demand.
#PlanningTask::duration
Read the duration.
#PlanningTask::resource
Read the resource identifier.
#PlanningTask::valid_window
Return whether the time window is valid.
#PlanningTask::with_window
Return a task with a release and due window.
#PortfolioAudit
pub struct PortfolioAudit {
candidates : Int
distinct : Int
retained : Int
pareto : Int
}A deterministic search audit record.
#PortfolioAudit::candidates
Read candidate count.
#PortfolioAudit::describe
Return a stable audit summary.
#PortfolioAudit::pareto
Read Pareto count.
#PortfolioAudit::retained
Read retained count.
#PortfolioDirection
pub enum PortfolioDirection {
Minimize
Maximize
}Solution portfolio and multi-objective selection utilities.
Finite-domain models frequently have several feasible schedules. This
module keeps a bounded, duplicate-free portfolio, supports lexicographic
and Pareto selection, and records deterministic search evidence for a CLI
or benchmark report.
#PortfolioObjectiveTerm
A named objective component.
#ProbeBatch
A bounded sequence of temporary model probes.
#ProbeBatch::describe
Render a probe batch for regression logs.
#ProbeBatch::results
Return a copy of recorded probe results.
#ProbeBatch::satisfiable_count
Count satisfiable probes.
#ProbeBatch::unsatisfiable_count
Count unsatisfiable probes.
#ProjectPlan
pub struct ProjectPlan {
solver : Solver
tasks : Array[PlanningTask]
starts : Array[Int]
dependencies : Array[PlanningDependency]
horizon : Int
capacities : Array[Int]
}A project plan with an exact finite-domain start model.
#ProjectPlan::add_dependency
Add a precedence relationship.
#ProjectPlan::critical_path_length
Return the critical-path makespan.
#ProjectPlan::critical_task_count
Return the number of critical tasks.
#ProjectPlan::critical_tasks
Return all zero-slack tasks.
#ProjectPlan::dependencies
Read copied dependencies.
#ProjectPlan::dependencies_valid
Return whether dependencies reference valid tasks.
#ProjectPlan::describe
Return a project summary for CLI diagnostics.
#ProjectPlan::earliest_starts
Compute earliest feasible starts using a critical-path pass.
#ProjectPlan::fix_start
Add a fixed assignment for a task start.
#ProjectPlan::has_cycle
Return whether the dependency network is acyclic.
#ProjectPlan::horizon
Read the planning horizon.
#ProjectPlan::is_valid_schedule
Return whether a concrete schedule is valid.
#ProjectPlan::latest_starts
Compute latest starts without extending the project horizon.
#ProjectPlan::makespan
Return a schedule's makespan.
#ProjectPlan::maximum_demand
Return the maximum resource demand.
#ProjectPlan::post_all_resource_non_overlap
Add non-overlap constraints for every resource.
#ProjectPlan::post_resource_non_overlap
Add pairwise non-overlap constraints for a resource's tasks.
#ProjectPlan::predecessors
Return tasks that directly precede a task.
#ProjectPlan::render
Return a stable task timeline.
#ProjectPlan::resource_profile
fn ProjectPlan::resource_profile(self : ProjectPlan, starts : Array[Int], resource : Int) -> Array[Int]Return resource usage at each time slot.
#ProjectPlan::resource_utilization
fn ProjectPlan::resource_utilization(self : ProjectPlan, starts : Array[Int], resource : Int) -> IntReturn resource utilization in integer percentage points.
#ProjectPlan::schedule
Extract start times from a solution.
#ProjectPlan::signature
Return a stable plan fingerprint.
#ProjectPlan::slacks
Return task slack from earliest and latest passes.
#ProjectPlan::solve
Solve the exact start model once.
#ProjectPlan::solve_all
Enumerate project schedules.
#ProjectPlan::solver
Return the underlying solver for application-specific constraints.
#ProjectPlan::start_variable
Read a start variable identifier.
#ProjectPlan::stats
Read the latest search statistics.
#ProjectPlan::successors
Return tasks that directly follow a task.
#ProjectPlan::task
Read a task.
#ProjectPlan::task_at
fn ProjectPlan::task_at(self : ProjectPlan, starts : Array[Int], resource : Int, time : Int) -> Int?Return the first task that uses a resource at a time.
#ProjectPlan::task_count
Read the number of tasks.
#ProjectPlan::tasks_on_resource
Return all task ids using a resource.
#ProjectPlan::topological_order
Return a topological task order, or None when dependencies cycle.
#ProjectPlan::total_load
Return total demand-time across all tasks.
#ProjectPlan::total_work
Return the total work across all tasks.
#ProjectPlan::validate_schedule
Validate a concrete schedule and return stable error codes.
#PruningExplanation
A collection of pruning reasons.
#PruningExplanation::add
Add a reason when not duplicated.
#PruningExplanation::describe
Render reasons.
#PruningExplanation::explains
Return whether a value has an explanation.
#PruningExplanation::for_variable
fn PruningExplanation::for_variable(self : PruningExplanation, variable : Int) -> Array[PruningReason]Return reasons for one variable.
#PruningExplanation::length
Return reason count.
#PruningReason
pub struct PruningReason {
variable : Int
value : Int
constraint : String
detail : String
}A reason for a domain value removal.
#QualityGateResult
pub struct QualityGateResult {
name : String
status : QualityStatus
observed : Int
threshold : Int
detail : String
}One quality-gate result.
#QualityGateResult::describe
Return a stable line.
#QualityGateResult::passed
Return whether this gate passes.
#QualityGateResult::status_name
Return a status label.
#QualityReview
A collection of quality-gate results.
#QualityReview::add
Add a gate.
#QualityReview::describe
Render the review.
#QualityReview::failure_count
Return failure count.
#QualityReview::maximum
fn QualityReview::maximum(self : QualityReview, name : String, observed : Int, threshold : Int, detail : String) -> UnitAdd a maximum threshold gate.
#QualityReview::minimum
fn QualityReview::minimum(self : QualityReview, name : String, observed : Int, threshold : Int, detail : String) -> UnitAdd a minimum threshold gate.
#QualityReview::note
Add a non-blocking informational warning.
#QualityReview::passed
Return whether the review passes.
#QualityReview::require
fn QualityReview::require(self : QualityReview, name : String, condition : Bool, detail : String) -> UnitAdd a boolean gate.
#QualityReview::results
Return copied results.
#QualityReview::score
Return a combined score where failures dominate.
#QualityReview::signature
Return a stable review signature.
#QualityReview::status_line
Return a concise review status.
#QualityReview::warning_count
Return warning count.
#QualityStatus
pub enum QualityStatus {
GatePassed
GateWarning
GateFailed
}Application quality gates for local acceptance and regression checks.
Gates turn model-level invariants into a compact report: feasibility,
coverage, deterministic signatures, and metric thresholds can be checked
together before a CLI or CI job declares a scenario ready.
#QueueSimulation
pub struct QueueSimulation {
horizon : Int
entities : Array[SimulationEntity]
queue : EventQueue
trace : Array[SimulationEvent]
next_sequence : Int
server_free : Int
total_wait : Int
completed : Int
}A single-server queue simulation.
#QueueSimulation::arrivals
Return all arrivals in id order.
#QueueSimulation::average_wait
Return average waiting time.
#QueueSimulation::completed
Return completed entity count.
#QueueSimulation::current_time
Return current simulation time.
#QueueSimulation::describe
Return a trace summary.
#QueueSimulation::entity_trace
Return all events for one entity.
#QueueSimulation::pending
Return queued entity count.
#QueueSimulation::run
Run until no event remains or the horizon is reached.
#QueueSimulation::schedule
fn QueueSimulation::schedule(self : QueueSimulation, time : Int, entity : Int, resource : Int, kind : SimulationEventKind, payload : Int) -> BoolSchedule an event.
#QueueSimulation::schedule_maintenance
fn QueueSimulation::schedule_maintenance(self : QueueSimulation, start : Int, duration : Int) -> BoolSchedule a maintenance block on the server.
#QueueSimulation::service_times
Return all service durations in id order.
#QueueSimulation::signature
Return a stable simulation signature.
#QueueSimulation::step
Process the next queued event.
#QueueSimulation::total_wait
Return total waiting time.
#QueueSimulation::trace
Return the event trace.
#QueueSimulation::utilization
Return a deterministic utilization percentage.
#RecipeResult
pub struct RecipeResult {
posted : Int
rejected : Int
}Constraint recipes for common operational rules.
#RelationTable
A validated extensional relation that can be posted to a solver.
#RelationTable::arity
Return relation arity.
#RelationTable::column
Return the projection of one relation column.
#RelationTable::compatible
Return the rows compatible with a partial tuple.
#RelationTable::contains
Return whether a row occurs in the relation.
#RelationTable::describe
Render rows for diagnostics.
#RelationTable::post
Post this relation for a tuple of variables.
#RelationTable::rows
Return a defensive copy of relation rows.
#ResourceMetric
pub struct ResourceMetric {
resource : Int
capacity : Int
total_load : Int
peak_load : Int
busy_slots : Int
horizon : Int
}Resource utilization and bottleneck metrics.
#ResourceMetric::average_percent
Return average utilization percentage.
#ResourceMetric::bottleneck_score
Return a bottleneck score.
#ResourceMetric::describe
Return a stable metric line.
#ResourceMetric::idle_slots
Return idle slot count.
#ResourceMetric::overloaded
Return whether capacity is exceeded.
#ResourceMetric::peak_percent
Return peak utilization percentage.
#ResourceSchedule
pub struct ResourceSchedule {
solver : Solver
tasks : Array[IntervalTask]
horizon : Int
capacity : Int
}A resource-constrained schedule with optional capacity checks.
#ResourceSchedule::add_flexible_task
fn ResourceSchedule::add_flexible_task(self : ResourceSchedule, name : String, duration : Int, demand : Int) -> Int?Add an interval with a full-horizon start window.
#ResourceSchedule::add_task
fn ResourceSchedule::add_task(self : ResourceSchedule, name : String, duration : Int, demand : Int, latest_start : Int) -> Int?Add an interval task and return its index.
#ResourceSchedule::avoid_window
fn ResourceSchedule::avoid_window(self : ResourceSchedule, window_start : Int, window_end : Int) -> BoolAdd an exclusive maintenance window to every task by fixing an allowed
start range that lies entirely before or after the window.
#ResourceSchedule::duration
Return task duration.
#ResourceSchedule::ends
Return task end times from a solution.
#ResourceSchedule::fix_start
Set an exact task start.
#ResourceSchedule::is_valid
Check every task and capacity slot in a complete schedule.
#ResourceSchedule::load_profile
Return a resource load profile.
#ResourceSchedule::precede
Add a precedence relation by tightening the successor start domain.
#ResourceSchedule::render
Render a Gantt-like one-line-per-task schedule.
#ResourceSchedule::solve
Solve the resource schedule once.
#ResourceSchedule::solve_all
Enumerate up to limit schedules.
#ResourceSchedule::start_variable
Return the start variable for a task.
#ResourceSchedule::starts
Return all task starts from a solution.
#ResourceSchedule::stats
Return statistics for the most recent solve.
#ResourceSchedule::summary
Return a stable task list summary.
#ResourceSchedule::task_count
Return the number of tasks.
#ResourceSchedule::task_name
Return task name.
#ResourceTimeline
pub struct ResourceTimeline {
horizon : Int
capacities : Array[Int]
intervals : Array[TimelineInterval]
}A collection of resource intervals.
#ResourceTimeline::add
Add an interval after bounds validation.
#ResourceTimeline::conflicts
Return all conflicting interval id pairs.
#ResourceTimeline::contains_id
Return whether an id exists.
#ResourceTimeline::feasible
Return whether all capacity rules hold.
#ResourceTimeline::find_slot
Return the earliest free slot of a requested duration.
#ResourceTimeline::free_slots
Return free time slots on one resource.
#ResourceTimeline::length
Return interval count.
#ResourceTimeline::load_at
Return the load at one resource and time.
#ResourceTimeline::makespan
Return the latest occupied time.
#ResourceTimeline::on_resource
fn ResourceTimeline::on_resource(self : ResourceTimeline, resource : Int) -> Array[TimelineInterval]Return intervals on one resource.
#ResourceTimeline::ordered
Return intervals sorted by start time.
#ResourceTimeline::overloads
Return all overload time points on a resource.
#ResourceTimeline::profile
Return a resource load profile.
#ResourceTimeline::render
Render resource intervals.
#ResourceTimeline::signature
Return a stable timeline fingerprint.
#ResourceTimeline::total_work
Return total work.
#ResourceTimeline::utilization
Return weighted utilization in integer percentage points.
#ResultCounter
pub struct ResultCounter {
accepted : Int
rejected : Int
}Small result helpers shared by CLI integrations.
#RiskBand
pub enum RiskBand {
LowRisk
MediumRisk
HighRisk
CriticalRisk
}Integer risk and sensitivity models for planning decisions.
Risk scores are explicit integer products of likelihood and impact. The
model is small enough for CI scenario checks and structured enough to feed
objective terms or an acceptance report.
#RiskItem
pub struct RiskItem {
id : Int
name : String
likelihood : Int
impact : Int
mitigation : Int
}#RiskScenario
A scenario with risk items and a resource budget.
#RiskScenario::above
Return items above a threshold.
#RiskScenario::band_counts
Return the band distribution.
#RiskScenario::describe
Return a stable scenario summary.
#RiskScenario::gross_score
Return total gross risk.
#RiskScenario::maximum_residual
Return highest residual risk.
#RiskScenario::mitigate
Apply extra mitigation to one item.
#RiskScenario::mitigation_value
Return marginal score reduction for mitigation.
#RiskScenario::priority_order
Return a priority order by residual risk.
#RiskScenario::residual_score
Return total residual risk.
#RiskScenario::signature
Return a scenario fingerprint.
#RosterModel
A worker/day/shift roster template built on the generic solver.
#RosterModel::configure
Configure the underlying search engine.
#RosterModel::coverage
Return a day/shift coverage matrix.
#RosterModel::day_count
Return the day count.
#RosterModel::day_variables
Return all variables for a day.
#RosterModel::fingerprint
Return a stable roster fingerprint.
#RosterModel::fix
Fix one worker/day assignment.
#RosterModel::is_valid
Check a solution against every roster constraint.
#RosterModel::post_daily_cover
Require exact worker coverage for each day and shift.
#RosterModel::post_daily_different
Require every worker to have a different shift on a day.
#RosterModel::post_no_consecutive_shift
Require adjacent assignments for one worker to use different shifts.
#RosterModel::post_pair_different
Prevent the same pair of workers from sharing a shift on a day.
#RosterModel::post_worker_interval
fn RosterModel::post_worker_interval(self : RosterModel, worker : Int, lower_shift : Int, upper_shift : Int, minimum : Int, maximum : Int) -> UnitBound a worker's number of assignments in a shift interval.
#RosterModel::post_worker_shift_bounds
fn RosterModel::post_worker_shift_bounds(self : RosterModel, worker : Int, shift : Int, minimum : Int, maximum : Int) -> UnitBound how often a worker may receive a shift type.
#RosterModel::post_worker_window_different
Require different shifts within a worker's rolling window.
#RosterModel::render
Render the roster as a stable worker-major table.
#RosterModel::score
Score a valid or partial roster using observable structural counters.
#RosterModel::shift_count
Return the number of shifts.
#RosterModel::shift_count_for
fn RosterModel::shift_count_for(self : RosterModel, solution : Solution, worker : Int, shift : Int) -> IntCount a shift for one worker.
#RosterModel::solve_all
Enumerate rosters up to a limit.
#RosterModel::stats
Return the latest search statistics.
#RosterModel::values
Read all assignments in worker-major order.
#RosterModel::variable
Return a worker/day variable id.
#RosterModel::worker_count
Return the worker count.
#RosterModel::worker_values
Read one worker's assignments.
#RosterModel::worker_variables
Return all variables for a worker.
#RosterModel::workload_spread
Compute the largest worker workload difference across shifts.
#RosterScore
pub struct RosterScore {
spread : Int
violations : Int
coverage_total : Int
}A compact roster quality score.
#RosterScore::coverage_total
Return the number of assigned worker/day cells represented by coverage.
#RosterScore::describe
Render a roster score.
#RosterScore::spread
Return the worker workload spread.
#RosterScore::violations
Return the number of detected violations.
#RoutingInstance
pub struct RoutingInstance {
points : Array[RoutingPoint]
depot : Int
vehicles : Int
capacities : Array[Int]
distances : Array[Array[Int]]
}A validated routing instance with an integer distance matrix.
#RoutingInstance::capacity
Read one vehicle capacity.
#RoutingInstance::customers
Return all customer identifiers, excluding the depot.
#RoutingInstance::depot
Return the depot location identifier.
#RoutingInstance::distance
Return the directed distance between two locations.
#RoutingInstance::location_count
Number of locations including the depot.
#RoutingInstance::point
Read one point.
#RoutingInstance::total_demand
Return the total demand of all customers.
#RoutingInstance::vehicle_count
Number of vehicles.
#RoutingPlan
A complete multi-vehicle plan.
#RoutingPlan::append
Append a stop to a vehicle route.
#RoutingPlan::describe
Return a stable route string useful for logs and benchmark snapshots.
#RoutingPlan::locate
Locate a customer in the plan, returning vehicle and position.
#RoutingPlan::route
Read a route.
#RoutingPlan::route_count
Read the number of routes.
#RoutingPlan::routes
Return copied routes.
#RoutingPlan::visited
Return all visits in vehicle order.
#RoutingPoint
pub struct RoutingPoint {
id : Int
x : Int
y : Int
demand : Int
service : Int
ready : Int
due : Int
}A finite-domain friendly vehicle-routing toolkit.
The routing layer deliberately keeps the data representation compact and
deterministic. It is useful before a full solver model is posted: a
planner can validate input, construct a feasible seed plan, measure it,
and then use the resulting route as a warm start for a richer model.
#RoutingPoint::id
Read the stable customer identifier.
#RoutingPoint::service
Read the service duration.
#RoutingPoint::valid_window
Return whether a point has a valid time window.
#RoutingPoint::with_service
Return a copy with a service duration.
#RoutingPoint::with_window
Return a copy with a time window.
#RoutingReport
pub struct RoutingReport {
distance : Int
makespan : Int
load : Int
visits : Int
errors : Array[String]
}Compact quality report for a routing plan.
#RoutingReport::describe
Return a stable summary string.
#RoutingReport::distance
Read the travel distance.
#RoutingReport::feasible
Return whether a report is feasible.
#RoutingReport::makespan
Read the route makespan.
#RoutingReport::visits
Read the visit count.
#Scenario
A named reusable model scenario for demos and acceptance smoke tests.
#Scenario::matches
Return whether the observed count matches the expected count.
#Scenario::solve
Solve a scenario with a cap.
#ScenarioAxis
A parameter axis for Cartesian scenario generation.
#ScenarioEvaluation
A scenario evaluation.
#ScenarioEvaluation::feasible
Return whether evaluation is feasible.
#ScenarioEvaluation::name
Return scenario name.
#ScenarioEvaluation::score
Return score.
#ScenarioValue
pub struct ScenarioValue {
name : String
value : Int
}Deterministic scenario generation for what-if model validation.
A scenario is a named vector of integer parameters. The engine supports
bounded Cartesian expansion, overrides, deltas, and reproducible scoring;
it is useful for capacity planning and regression matrices in CI.
#SchedulePreference
pub struct SchedulePreference {
day : Int
shift : Int
preferred_person : Int
penalty : Int
}A small weighted preference used by application code to rank schedules.
#ScheduleProblem
A compact multi-day staff scheduling model.
Each slot is an integer variable containing the assigned worker. The
builder adds per-day uniqueness, workload bounds, and optional rotation
constraints while leaving the model open for application-specific rules.
#ScheduleProblem::assignments
Convert a solution to a day-by-day integer matrix.
#ScheduleProblem::avoid_pair
Add a rule that two workers cannot share a day.
#ScheduleProblem::avoid_same_shift
Add a rule that the same shift rotates between consecutive days.
#ScheduleProblem::fix
Add an exact assignment for one day and shift.
#ScheduleProblem::is_complete
Return whether an assignment covers every slot exactly once.
#ScheduleProblem::maximum_load
Read the configured workload upper bound.
#ScheduleProblem::minimum_load
Read the configured workload lower bound.
#ScheduleProblem::render
Render a solution as a stable, human-readable grid.
#ScheduleProblem::score
fn ScheduleProblem::score(self : ScheduleProblem, solution : Solution, preferences : Array[SchedulePreference]) -> IntScore a solution by summing penalties for preference violations.
#ScheduleProblem::slot
Return a slot variable identifier.
#ScheduleProblem::slot_ids
Return all slot identifiers in day-major order.
#ScheduleProblem::solve
Solve the schedule once.
#ScheduleProblem::solve_all
Enumerate schedule solutions.
#ScheduleProblem::solver
Return the underlying model for additional constraints.
#ScheduleProblem::stats
Return solver counters for the most recent schedule solve.
#SchemaField
pub struct SchemaField {
id : Int
name : String
lower : Int
upper : Int
default : Int
}Schema helpers for named finite-domain models.
A schema is a stable, serializable view of variables and defaults. It is
useful for CLI forms, configuration validation, and checking that a saved
solution still matches the model shape after a library upgrade.
#SchemaField::domain
Return its finite domain.
#SchemaField::valid
Return whether the field is valid.
#ScoredSolution
A score paired with a solution for reporting and ranking.
#ScoredSolution::describe
Return a stable score report.
#ScoredSolution::rank
Return the one-based display rank.
#ScoredSolution::score
Return the scalar score.
#ScoredSolution::solution
Return the underlying solution.
#SearchConfig
pub struct SearchConfig {
max_solutions : Int
variable_heuristic : VariableHeuristic
value_heuristic : ValueHeuristic
propagation_rounds : Int
enable_learning : Bool
node_limit : Int
} derive(Debug)Search behavior that is stable across all supported backends.
#SearchConfig::choose_values
Select a value ordering heuristic.
#SearchConfig::choose_variables
fn SearchConfig::choose_variables(self : SearchConfig, heuristic : VariableHeuristic) -> SearchConfigSelect a variable heuristic.
#SearchConfig::learning
Enable or disable lightweight conflict learning.
#SearchConfig::limit
Set the solution limit.
#SearchConfig::node_budget
Cap visited search nodes for interactive or service workloads.
#SearchConfig::round_limit
Set the maximum number of propagation rounds at one search node.
#SearchStats
pub struct SearchStats {
nodes : Int
failures : Int
propagations : Int
constraint_checks : Int
pruned_values : Int
solutions : Int
maximum_depth : Int
restarts : Int
learned_conflicts : Int
truncated : Bool
} derive(Debug)Counters collected by the solver during the most recent run.
#SearchStats::check_count
Number of individual constraint evaluations.
#SearchStats::depth
Maximum recursion depth reached.
#SearchStats::describe
Render statistics as stable key-value output for CI and benchmarks.
#SearchStats::failure_count
Number of rejected branches.
#SearchStats::is_truncated
Whether a node or propagation budget stopped the search.
#SearchStats::node_count
Number of visited search nodes.
#SearchStats::propagation_count
Number of domain propagation passes.
#SearchStats::pruned_count
Number of values removed by propagation.
#SearchStats::solution_count
Number of solutions found.
#SequenceModel
A reusable finite-domain sequence model. It is useful for quotas,
transition planning, configuration strings and small scheduling horizons.
#SequenceModel::configure
Set the search configuration.
#SequenceModel::domain_of
Return the current domain of a sequence position.
#SequenceModel::fingerprint
Return a compact sequence fingerprint.
#SequenceModel::fix
Bind a position to a constant value.
#SequenceModel::is_valid
Check a solution against the sequence model.
#SequenceModel::length
Return the number of positions.
#SequenceModel::lower_bound
Return the value lower bound.
#SequenceModel::post_allowed_values
Restrict every sequence position to an explicit finite set.
#SequenceModel::post_at_least_count
Require at least count positions to contain value.
#SequenceModel::post_at_most_count
Require at most count positions to contain value.
#SequenceModel::post_constant_step
Require every adjacent difference to equal distance.
#SequenceModel::post_edge_transitions
fn SequenceModel::post_edge_transitions(self : SequenceModel, tables : Array[Array[Array[Int]]]) -> UnitAllow a different transition table at each edge.
#SequenceModel::post_exact_count
Require exactly count positions to contain value.
#SequenceModel::post_no_adjacent_equal
Require consecutive positions to differ.
#SequenceModel::post_nondecreasing
Require a nondecreasing sequence.
#SequenceModel::post_run_limit
Bound the length of every same-value run.
#SequenceModel::post_strictly_increasing
Require a strictly increasing sequence.
#SequenceModel::post_sum
Post an exact weighted sum over all sequence values.
#SequenceModel::post_sum_between
Restrict the total sequence sum to an inclusive interval.
#SequenceModel::post_transitions
Allow only the supplied adjacent transition pairs.
#SequenceModel::post_window_count
fn SequenceModel::post_window_count(self : SequenceModel, width : Int, value : Int, count : Int) -> UnitRequire a window to contain exactly count occurrences of value.
#SequenceModel::post_window_sum
Require a window to have an exact sum at every start position.
#SequenceModel::post_window_table
fn SequenceModel::post_window_table(self : SequenceModel, width : Int, rows : Array[Array[Int]]) -> UnitPost a table relation over one or more consecutive windows.
#SequenceModel::render
Render a sequence as a space-separated row.
#SequenceModel::solution_summary
Return every value used by a complete solution.
#SequenceModel::solve
Solve for one sequence.
#SequenceModel::solve_all
Enumerate up to limit sequences.
#SequenceModel::stats
Return the latest search statistics.
#SequenceModel::upper_bound
Return the value upper bound.
#SequenceModel::values_of
Extract sequence values from a solution.
#SequenceModel::variables
Return a copy of sequence variable identifiers.
#SequenceReport
pub struct SequenceReport {
length : Int
minimum : Int
maximum : Int
transitions : Int
runs : Int
checksum : Int
}Sequence analytics for traces, rosters, and solver decisions.
The routines are allocation-conscious for small integer traces and expose
stable edit, transition, run, and normalization metrics used by diagnostics.
#SequenceReport::describe
Return a stable sequence summary.
#SequenceSummary
Summary statistics for a complete sequence.
#SequenceSummary::changes
Return the number of adjacent changes.
#SequenceSummary::describe
Render a sequence summary.
#SequenceSummary::maximum
Return the maximum.
#SequenceSummary::minimum
Return the minimum.
#SequenceSummary::runs
Return the number of same-value runs.
#SequenceSummary::total
Return the sum.
#SequenceSummary::values
Return the values in a sequence summary.
#ServiceMetrics
pub struct ServiceMetrics {
orders : Int
on_time : Int
late : Int
total_lateness : Int
maximum_lateness : Int
quantity : Int
cost : Int
}A service-level aggregate.
#ServiceMetrics::average_lateness
Return average lateness.
#ServiceMetrics::describe
Return a stable metrics line.
#ServiceMetrics::late_percent
Return late percentage.
#ServiceMetrics::meets_target
Return whether a target service level is met.
#ServiceMetrics::on_time_percent
Return on-time percentage.
#ServiceObservation
pub struct ServiceObservation {
id : Int
promised : Int
actual : Int
quantity : Int
penalty : Int
}Service-level metrics for operational plans.
#ServiceObservation::cost
Return weighted lateness penalty.
#ServiceObservation::describe
Return a stable observation line.
#ServiceObservation::earliness
Return earliness.
#ServiceObservation::lateness
Return lateness.
#ServiceObservation::on_time
Return on-time status.
#SetCovering
A minimum-cost set-covering model.
#SetCovering::cost
Return total covering cost.
#SetCovering::covers_all
fn SetCovering::covers_all(self : SetCovering, solution : Solution, covers : Array[Array[Int]]) -> BoolReturn whether every universe element is covered.
#SetCovering::render
Render a covering solution.
#SetCovering::selected_sets
Return selected set indices.
#SetCovering::solve
Solve for a minimum-cost covering.
#SetCovering::stats
Return solver statistics.
#SimulationEntity
pub struct SimulationEntity {
id : Int
arrival : Int
service : Int
priority : Int
}A simulated entity record.
#SimulationEvent
pub struct SimulationEvent {
time : Int
sequence : Int
entity : Int
resource : Int
kind : SimulationEventKind
payload : Int
}A scheduled event.
#SimulationEvent::kind_name
Return a stable kind label.
#SimulationEventKind
pub enum SimulationEventKind {
Arrival
ServiceStart
ServiceFinish
Maintenance
CustomEvent
}Deterministic discrete-event simulation helpers.
The simulation layer is intentionally integer-based: it can replay queue,
service, and maintenance scenarios in CI without wall-clock randomness.
Events are ordered by time and then sequence number, making traces stable
even when several events share a timestamp.
#Solution
A complete assignment returned by the solver.
#Solution::csv
Render a solution as comma-separated values.
#Solution::describe
Format a solution as name=value pairs using a model's variable names.
#Solution::get
Read a variable's assigned value from a solution.
#SolutionDelta
pub struct SolutionDelta {
variable : Int
before : Int
after : Int
}A named difference between two complete assignments.
#SolutionDelta::after
Read a delta's new value.
#SolutionDelta::before
Read a delta's old value.
#SolutionDelta::describe
Return a readable delta line.
#SolutionDelta::variable
Read a delta variable id.
#SolutionPool
pub struct SolutionPool {
records : Array[SolutionRecord]
limit : Int
terms : Array[PortfolioObjectiveTerm]
}A bounded solution collection.
#SolutionPool::best
Return the best record.
#SolutionPool::contains
Return whether a solution value vector is already stored.
#SolutionPool::insert
Add a scored solution and keep the best bounded set.
#SolutionPool::is_full
Return whether the pool is full.
#SolutionPool::length
Return the number of stored solutions.
#SolutionPool::objective_count
Return the number of objective dimensions.
#SolutionPool::pareto_front
Return the Pareto-nondominated records.
#SolutionPool::records
Return copied records in objective order.
#SolutionPool::signature
Return a pool fingerprint.
#SolutionPool::worst
Return the worst record currently retained.
#SolutionRecord
A scored solution record.
#SolutionRecord::scores
Read objective scores.
#SolutionRecord::signature
Return a stable record signature.
#SolutionRecord::values
Read the copied solution values.
#SolveAttempt
pub struct SolveAttempt {
index : Int
solved : Bool
solution_count : Int
work : Int
signature : Int
}Repeatable model execution sessions.
A session records attempts, signatures, and solver counters around a model.
It is intended for local acceptance runs and for collecting evidence that a
change remains deterministic across repeated solves.
#SolveAttempt::describe
Return a stable attempt line.
#SolveAudit
A compact audit record for a solve operation.
#SolveAudit::complete
Return whether the search was not truncated.
#SolveAudit::describe
Return a stable audit summary.
#SolveAudit::satisfiable
Return whether the capped search found at least one solution.
#SolveAudit::solutions
Return the number of solutions in an audit.
#SolveAudit::stats
Return search statistics from an audit.
#SolveSession
A repeated execution session.
#SolveSession::all_solved
Return whether all attempts solved.
#SolveSession::attempts
Return attempts.
#SolveSession::average_work
Return average work.
#SolveSession::describe
Return a compact session report.
#SolveSession::deterministic
Return whether all signatures agree.
#SolveSession::maximum_work
Return the largest work attempt.
#SolveSession::review
Return a quality review for a session.
#SolveSession::run
Execute a solver once and record its result.
#SolveSession::signature
Return a session fingerprint.
#SolveSession::total_work
Return total work.
#SolveSession::work_stable
Return whether work is stable within a tolerance.
#SolveStatus
Public status of a guarded solve.
#Solver
pub struct Solver {
variables : Array[Variable]
constraints : Array[Constraint]
max_solutions : Int
search_config : SearchConfig
last_stats : SearchStats
}The mutable model and deterministic finite-domain search engine.
#Solver::add_constraint
Add a constraint after checking all referenced variable identifiers.
#Solver::add_variable
Add a variable and return its zero-based identifier.
#Solver::assign
Assign a value before solving. This is useful for givens in application
models such as Sudoku and for incremental model construction.
#Solver::audit
Run a capped solve and return its audit record.
#Solver::best_solution
Return the best solution after bounded enumeration.
#Solver::compare_heuristics
Compare the built-in variable heuristics on the same model.
#Solver::compare_value_orders
Compare the built-in value ordering heuristics.
#Solver::configure
Replace the search configuration for future solves.
#Solver::constraint_count
Number of posted constraints in the model.
#Solver::constraint_csv
Render a solver's structural report as CSV-like rows.
#Solver::constraint_kind_counts
Count constraints by semantic family.
#Solver::constraint_report
Return one diagnostic line for each posted constraint.
#Solver::constraints
Return a copy of all posted constraints.
#Solver::count_solutions
Count solutions up to a cap while avoiding a large result allocation.
#Solver::csv_header
Render all variable names as a CSV header.
#Solver::diagnostic_report
Produce a complete model report suitable for a CLI or issue template.
#Solver::domain_csv
Return a compact domain report.
#Solver::domain_histogram
Return a compact domain histogram for model review.
#Solver::domain_of
Read a variable domain from the model without exposing mutable internals.
#Solver::domains
Return a copy of all current variable domains.
#Solver::enumeration_complete
Return whether the capped enumeration was complete.
#Solver::explain_assumptions
Find a deletion-minimal subset that still makes the model unsatisfiable.
The operation is deliberately conservative: it only removes an item when
the remaining set is still unsatisfiable.
#Solver::guarded_solve
Solve one model while enforcing both structural and node limits.
#Solver::has_nonempty_domains
Return whether every variable has at least one candidate.
#Solver::is_consistent_with
Test a temporary decision set without exposing a solution.
#Solver::is_satisfiable
Return whether at least one solution exists.
#Solver::is_valid_solution
Verify a complete solution against variable domains and constraints.
#Solver::last_pruned_values
Return the number of candidate values removed by the last solve.
#Solver::last_solve_truncated
Return whether the last solve reached a budget limit.
#Solver::limit
Set the maximum number of solutions collected by solve_all.
#Solver::limit_violation
Return the first limit category exceeded by a model.
#Solver::metrics
Calculate structural metrics before solving.
#Solver::metrics_map
Return a two-column map that can be emitted by a metrics adapter.
#Solver::optimize
fn Solver::optimize(self : Solver, variable : Int, direction : OptimizationDirection) -> OptimizationResult?Enumerate solutions and return the one with the best objective value.
#Solver::performance_report
Render metrics and latest search statistics together.
#Solver::probe
Probe and record a decision set.
#Solver::ranked_solutions
fn Solver::ranked_solutions(self : Solver, objective : Objective, limit : Int) -> Array[ScoredSolution]Enumerate and rank solutions by a multi-component objective.
#Solver::readiness_report
Render a model readiness check.
#Solver::ready
Return whether a model is ready to enter search.
#Solver::reset_stats
Reset counters without changing the model.
#Solver::restore
Restore a snapshot captured from this solver's variable layout.
#Solver::snapshot
Save the current domains and search configuration.
#Solver::solution_csv
Render a named solution as a two-line CSV document.
#Solver::solve
Find one solution, if the model is satisfiable.
#Solver::solve_all
Find up to the configured number of solutions using propagation and MRV.
#Solver::solve_all_with_assumptions
fn Solver::solve_all_with_assumptions(self : Solver, set : AssumptionSet, limit : Int) -> Array[Solution]Enumerate solutions under temporary decisions.
#Solver::solve_with
Solve with an explicit configuration.
#Solver::solve_with_assumptions
Apply a set of decisions, solve once, and restore every original domain.
#Solver::solve_with_budget
Solve with a node budget and expose truncation explicitly through stats.
#Solver::stats
Get counters from the most recent solve.
#Solver::structural_fingerprint
Return a stable textual statistic for a model's structure.
#Solver::summary
Return a concise model summary for CLI diagnostics.
#Solver::validate
Validate the model's static shape without running search.
#Solver::validation_error
Validate a model and return a human-readable failure reason.
#Solver::variable_count
Number of variables in the model.
#Solver::variables
Return a copy of the current variable declarations.
#Solver::violations
Return labels for every constraint violated by a complete solution.
#Solver::within_limits
Check whether a model fits configured structural limits.
#SolverMetrics
pub struct SolverMetrics {
variables : Int
constraints : Int
total_candidates : Int
singleton_variables : Int
empty_variables : Int
global_constraints : Int
arithmetic_constraints : Int
scheduling_constraints : Int
}Structural metrics for monitoring model growth and propagation quality.
#SolverMetrics::average_domain_size
Return average candidate count rounded down.
#SolverMetrics::branching_estimate
Return a rough branching estimate.
#SolverMetrics::describe
Return a stable metrics line.
#SolverMetrics::fixed_percentage
Return the fraction of variables already fixed as a percentage.
#SolverMetrics::has_empty_domain
Return whether the model contains an empty domain.
#SolverMetrics::health_score
Calculate a simple model health score from 0 to 100.
#Sudoku
A standard 9x9 Sudoku model backed by the finite-domain solver.
#Sudoku::add_diagonals
Add the two main diagonal constraints for a variant Sudoku.
#Sudoku::add_distance
Add a pair of cells that must differ by an exact distance.
#Sudoku::cell
Return the cell identifier at row and column coordinates.
#Sudoku::cell_ids
Return the internal cell identifiers in row-major order.
#Sudoku::compact
Render a compact 81-character solution string.
#Sudoku::count_solutions
Count solutions up to a cap without exposing the whole solution array.
#Sudoku::givens
Return the original givens as 81 digits, with zero for blanks.
#Sudoku::is_valid
Check whether a solution obeys every row, column and box rule.
#Sudoku::render
Render a solved board with three-by-three separators.
#Sudoku::solve_all
Return up to limit Sudoku solutions.
#Sudoku::solver
Return the underlying solver for advanced variants.
#Sudoku::stats
Return search counters for the most recent solve.
#TextDocument
A text document with line access.
#TextDocument::line
Return one line.
#TextDocument::line_count
Return line count.
#TextDocument::lines
Return copied lines.
#TextDocument::nonempty_lines
Return non-empty line numbers.
#TextDocument::without_comments
Return comment-free lines with a configured prefix.
#TextToken
pub struct TextToken {
value : String
line : Int
column : Int
}Dependency-free text and delimited-data utilities.
Model files and benchmark artifacts often need a tiny parser but should not
pull an IO or JSON dependency into the core package. These routines cover
stable tokenization, key-value configuration, CSV rows, and redaction.
#TimelineInterval
pub struct TimelineInterval {
id : Int
resource : Int
start : Int
end : Int
weight : Int
label : String
}Interval analytics for calendars, reservations, and resource timelines.
Timeline records are half-open intervals [start, end). Keeping this
convention explicit makes boundary-touching reservations composable and
lets capacity checks share the same logic as scheduling constraints.
#TimelineInterval::contains
Return whether a time point is covered.
#TimelineInterval::duration
Return interval duration.
#TimelineInterval::valid
Return whether an interval is valid.
#UtilityItem
pub struct UtilityItem {
id : Int
weight : Int
value : Int
}A weighted item used by integer knapsack helpers.
#UtilityItem::density
Return value density using a scale factor.
#ValidationIssue
pub struct ValidationIssue {
code : String
path : String
message : String
severity : ValidationSeverity
}One validation finding.
#ValidationIssue::code
Read issue code.
#ValidationIssue::describe
Render an issue for logs.
#ValidationIssue::message
Read issue message.
#ValidationIssue::path
Read issue path.
#ValidationIssue::severity_name
Return a stable severity label.
#ValidationReport
A collection of findings.
#ValidationReport::describe
Render all issues as newline-separated lines.
#ValidationReport::error
fn ValidationReport::error(self : ValidationReport, code : String, path : String, message : String) -> UnitAdd an error.
#ValidationReport::error_count
Count errors.
#ValidationReport::info
fn ValidationReport::info(self : ValidationReport, code : String, path : String, message : String) -> UnitAdd an informational note.
#ValidationReport::issues
Return copied issues.
#ValidationReport::length
Return issue count.
#ValidationReport::push
Add an issue.
#ValidationReport::signature
Return a stable report fingerprint.
#ValidationReport::valid
Return whether no errors exist.
#ValidationReport::warning
fn ValidationReport::warning(self : ValidationReport, code : String, path : String, message : String) -> UnitAdd a warning.
#ValidationReport::warning_count
Count warnings.
#ValidationSeverity
pub enum ValidationSeverity {
ValidationInfo
ValidationWarning
ValidationError
}Reusable validation and diagnostics for application-facing APIs.
Validation results carry machine-readable codes and paths so command-line
clients can show actionable failures without parsing exception text. The
report is immutable from a caller's perspective but uses arrays internally
for low-allocation model construction.
#ValueHeuristic
Value ordering strategy used when branching on a variable.
#Variable
A named finite-domain variable.
#Variable::clone
Return an independent copy of the variable and its domain.
#Variable::domain
Return the current domain of a variable.
#Variable::name
Return a variable's stable user-facing name.
#VariableGroup
A reusable collection of variable identifiers for application builders.
#VariableGroup::all_different
Post AllDifferent for the group.
#VariableGroup::count
Post a count constraint for the group.
#VariableGroup::ids
Return identifiers in group order.
#VariableGroup::length
Number of variables in the group.
#VariableGroup::sum
Post a target sum for the group.
#VariableHeuristic
Variable selection strategy used by the depth-first search engine.
#VehicleRoute
A route is represented without an implicit depot in its stop list.
#VehicleRoute::insert
Insert a customer at a bounded position.
#VehicleRoute::length
Number of customer visits.
#VehicleRoute::push_unique
Append a customer when it has not already been used in this route.
#VehicleRoute::remove
Remove a stop and return whether it was found.
#VehicleRoute::reverse_segment
Reverse a bounded inclusive segment for 2-opt neighborhoods.
#VehicleRoute::stops
Read a copied stop sequence.
#VehicleRoute::vehicle
Read the vehicle index.
#WeightedGraph
A bounded weighted graph.
#WeightedGraph::add_edge
Add or replace an edge.
#WeightedGraph::average_degree
Return the average outgoing degree.
#WeightedGraph::breadth_first_order
Breadth-first traversal from a source.
#WeightedGraph::connected
Return whether a graph has a path between two vertices.
#WeightedGraph::connected_components
Return connected components, treating directed edges as undirected links.
#WeightedGraph::degree
Return the out-degree.
#WeightedGraph::depth_first_order
Depth-first traversal from a source.
#WeightedGraph::dijkstra_distances
Compute non-negative shortest distances from a source with Dijkstra.
#WeightedGraph::dijkstra_predecessors
Compute shortest-path predecessors from a source.
#WeightedGraph::distance_sum
Return the sum of distances from a source to reachable vertices.
#WeightedGraph::edge_count
Return the total edge count.
#WeightedGraph::edge_list
Return a stable edge-list representation.
#WeightedGraph::edge_weight
Return an edge weight.
#WeightedGraph::edges
Return all graph edges in stable row-major order.
#WeightedGraph::floyd_warshall
Compute all-pairs shortest distances.
#WeightedGraph::graph_signature
Return a stable graph fingerprint.
#WeightedGraph::greedy_coloring
Greedily color vertices in order.
#WeightedGraph::has_directed_cycle
Return whether the directed graph contains a cycle.
#WeightedGraph::has_edge
Return whether an edge exists.
#WeightedGraph::highest_degree_vertex
Return the vertex with the largest degree.
#WeightedGraph::is_directed
Return whether edges are directed.
#WeightedGraph::is_simple_graph
Return whether the graph is a simple graph.
#WeightedGraph::isolated_vertex_count
Return the number of isolated vertices.
#WeightedGraph::matrix
Return a copied adjacency matrix using -1 for absent edges.
#WeightedGraph::maximum_flow
Find a maximum flow between source and sink.
#WeightedGraph::maximum_outgoing_weight
Return the heaviest outgoing edge weight from a vertex.
#WeightedGraph::minimum_outgoing_weight
Return the lightest outgoing edge weight, or -1 when isolated.
#WeightedGraph::minimum_spanning_forest
Return a minimum spanning forest using a deterministic Kruskal pass.
#WeightedGraph::neighbors
Return outgoing neighbors in ascending vertex order.
#WeightedGraph::reaches_all
Return whether all vertices are reachable from a source.
#WeightedGraph::remove_edge
Remove an edge.
#WeightedGraph::shortest_path
fn WeightedGraph::shortest_path(self : WeightedGraph, source : Int, destination : Int) -> Array[Int]?Reconstruct a shortest path from source to destination.
#WeightedGraph::topological_order
Return an ordering for a directed acyclic graph, or None for a cycle.
#WeightedGraph::transitive_closure
Compute transitive reachability using boolean closure.
#WeightedGraph::valid_coloring
Validate a vertex coloring.
#WeightedGraph::valid_vertex
Validate a vertex identifier.
#WeightedGraph::vertex_count
Return the vertex count.
#WhatIfScenario
A named scenario.
#WhatIfScenario::delta
Return a scenario with a delta applied.
#WhatIfScenario::get
Read a scenario parameter.
#WhatIfScenario::set
Set or replace a scenario parameter.
#WhatIfScenario::signature
Return a stable scenario key.
#WhatIfScenario::values
Return copied values.
#absolute
Bind result to the absolute value of source.
#active_resource_count
Return the number of active resources at a time.
#aggregate_model_reports
Aggregate reports.
#all_different
Require all listed variables to be pairwise different.
#all_different_pairs
Generate pairwise inequality constraints for a variable group.
#all_in_range
Return whether values stay inside a range.
#allocation_bin_value
Return the total value in a bin.
#allocation_bin_weight
Return the total weight in a bin.
#allocation_completion
Return the fraction of items assigned as integer percentage points.
#allocation_feasible
Return whether every item is assigned and every rule holds.
#allocation_group_count
fn allocation_group_count(instance : AllocationInstance, plan : AllocationPlan, bin : Int, group : Int) -> IntCount items from a group in a bin.
#allocation_instance
fn allocation_instance(items : Array[AllocationItem], capacities : Array[Int]) -> AllocationInstance?Create an allocation instance with no group limits.
#allocation_item
Construct an allocation item.
#allocation_items_by_value
Return items ordered by descending value.
#allocation_loads
Return all bin loads.
#allocation_plan
Create an unassigned plan.
#allocation_report
Return a stable allocation report.
#allocation_signature
Return a stable one-line plan representation.
#allocation_spread
Return max load minus min load.
#allocation_values
Return all bin values.
#allowed_rows
Generate a table relation for an array of variables.
#allowed_values
Restrict a variable to an explicit finite set.
#alternating_binary_sequence
Build a balanced binary sequence with no adjacent equal positions.
#arithmetic_relation
fn arithmetic_relation(left_domain : Domain, right_domain : Domain, operation : (Int, Int) -> Int) -> RelationTable?Build a Cartesian arithmetic table for a custom pure function.
#arithmetic_result_domain
Return a finite domain containing all results of a binary operation.
#artifact_benchmark
fn artifact_benchmark(name : String, work : Int, solved : Bool, signature : Int) -> ArtifactBenchmarkCreate a benchmark artifact.
#assignment_alternates
Return whether a sequence alternates between two labels.
#assignment_category_count
Count distinct categorical labels.
#assignment_change_penalty
Return a weighted change penalty.
#assignment_changes
Count differences at matching positions.
#assignment_counts
Return category counts for labels from 0 through category_count - 1.
#assignment_hamming_distance
Return Hamming distance between assignments.
#assignment_imbalance
Return a categorical load imbalance score.
#assignment_metrics
fn assignment_metrics(values : Array[Int], category_count : Int, previous : Array[Int]) -> AssignmentMetricsSummarize an assignment sequence.
#assignment_problem
Build a rectangular assignment model from a row-major cost matrix.
#assignment_signature
Return a stable categorical fingerprint.
#assignment_transitions
Return the number of adjacent label changes.
#assumption
Construct a variable/value decision.
#assumptions_from
Create an assumption set from an array. Duplicate variables are rejected
by retaining the first decision and ignoring later duplicates.
#at_least_value
Require at least count variables to take value.
#at_most_value
Require at most count variables to take value.
#attempts_equal
Return whether two attempts agree on outcome and signature.
#average_customer_demand
Return the average customer demand using integer division.
#average_route_distance
Return the average route distance using integer division.
#balance_unassigned
Balance all currently unassigned items onto the least loaded bins.
#balanced_schedule
Create a balanced schedule model.
#benchmark_artifact_suite
Create a suite.
#benchmark_coloring
Run a grid coloring benchmark.
#benchmark_evidence
Build benchmark evidence.
#benchmark_fingerprint
Return a stable fingerprint useful for regression tests.
#benchmark_latin
Run a small Latin-square benchmark.
#benchmark_markdown
Render benchmark records as Markdown table rows.
#benchmark_n_queens
Run the canonical N-Queens benchmark.
#benchmark_regressions
fn benchmark_regressions(baseline : BenchmarkArtifactSuite, candidate : BenchmarkArtifactSuite, tolerance : Int) -> Array[String]Return cases whose work regressed beyond tolerance.
#benchmark_report
Render the suite as plain text for CI logs.
#benchmark_result
fn benchmark_result(name : String, solved : Bool, solutions : Int, stats : SearchStats, repeat_count : Int) -> BenchmarkResultConstruct a benchmark result from solver counters.
#benchmark_schedule
Run a small balanced-schedule benchmark.
#benchmark_suite
Return the canonical benchmark set.
#benchmark_work
Return total deterministic work across a suite.
#benchmarks_pass
Return whether every benchmark in a suite solved successfully.
#best_fit_bin
Return the tightest bin that can receive an item.
#best_fit_decreasing
Allocate items in descending weight order using best fit.
#best_heuristic
Return the result with the lowest deterministic work score.
#best_heuristic_nodes
Return the deterministic node count of the best configuration.
#best_objective_row
fn best_objective_row(rows : Array[Array[Int]], directions : Array[PortfolioDirection]) -> Array[Int]?Return a lexicographically best row.
#best_scenario
Select the best feasible evaluation.
#best_score
Return the best score for one objective.
#best_solution
Return a single best solution under objective terms.
#between
Restrict a variable to an inclusive interval.
#bin_packing
Build a fixed-bin packing problem.
#binary_sequence
Build a binary sequence with exactly ones true positions.
#binary_transitions
Return a canonical transition table for a binary automaton.
#bipartite_matching
fn bipartite_matching(left_size : Int, right_size : Int, relation : FiniteRelation) -> Array[RelationPair]Return matched left-right pairs from a unit flow.
#bipartite_network
fn bipartite_network(left_size : Int, right_size : Int, relation : FiniteRelation) -> CapacityNetworkBuild a bipartite compatibility network.
#boolean_domain
Boolean relation helpers built on extensional finite-domain tables.
#boolean_indicators
Convert booleans to integer indicators.
#boolean_truth_table
Return a Boolean truth table for a binary operation.
#boolean_variable
Create a Boolean variable.
#bottleneck_order
Return resource ids sorted by bottleneck score.
#burst_queue
Build a deterministic burst-arrival queue.
#calendar_active
Return all windows that contain a time.
#calendar_chain
Build a simple precedence chain.
#calendar_clip
fn calendar_clip(windows : Array[CalendarWindow], lower : Int, upper : Int) -> Array[CalendarWindow]Return windows within a horizon.
#calendar_covered_duration
Return total covered duration.
#calendar_first_free
Return the first free start for a duration after a time.
#calendar_gap_duration
Return total gap duration between windows.
#calendar_model
Create an empty calendar.
#calendar_non_overlapping
Return whether windows are non-overlapping.
#calendar_peak_concurrency
Return the maximum concurrency.
#calendar_slots
Return a regular slot grid.
#calendar_summary
Return a calendar summary.
#calendar_window
Create a calendar window.
#calendar_window_signature
Return a window set fingerprint.
#capacity_arc
Create a capacity arc.
#capacity_constraint
Generate a cumulative capacity constraint.
#capacity_network
Create an empty network.
#capacity_slack
Return the largest capacity residual among routes.
#category_run_lengths
Return each category's longest consecutive run.
#checkerboard_matrix
Create a checkerboard matrix.
#classic_solution_string
fn classic_solution_string() -> StringReturn a solved canonical board for regression tests.
#classic_sudoku
Return a canonical puzzle used in examples and benchmark baselines.
#clique_lower_bound
Return the chromatic lower bound from a clique supplied by the caller.
#collect_solution_pool
fn collect_solution_pool(solver : Solver, limit : Int, terms : Array[PortfolioObjectiveTerm]) -> SolutionPoolEnumerate solutions into a bounded portfolio.
#coloring_count
Return the number of colors in a coloring.
#common_domain_values
Return values common to all domains.
#compare_benchmark_suites
fn compare_benchmark_suites(baseline : BenchmarkArtifactSuite, candidate : BenchmarkArtifactSuite) -> Array[(String, Int, Int)]Compare two suites by case name and work.
#compare_domain_arrays
Compare two domain arrays.
#compare_evidence
Compare two evidence bundles.
#compare_service_sets
fn compare_service_sets(baseline : Array[ServiceObservation], candidate : Array[ServiceObservation]) -> StringReturn a report comparing two observation sets.
#complete_coloring
Build a complete graph where every pair must receive different colors.
#compose_relations
Compose two relations.
#constraint_info
Build a structured diagnostic record.
#constraint_kind
Return the semantic family of a constraint.
#constraint_label
Return a stable public label for a constraint.
#constraint_recipe
Create a recipe around a builder.
#constraint_variables
Return all variable ids referenced by a constraint in stable order.
#count_bundle
Generate an exact count and its useful at-most/at-least decomposition.
#count_value
Require exactly count variables to take value.
#covered_duration
Return the total covered duration after merging intervals.
#critical_operations
fn critical_operations(instance : ManufacturingInstance, schedule : ManufacturingSchedule) -> Array[Int]Return the critical operation ids by finish time.
#cumulative
Limit the sum of active demands for interval tasks.
#custom_event_kind
Return the custom-event constructor for callers building integrations.
#customers_by_depot_distance
Return customers sorted by distance from the depot.
#cycle_coloring
Build a cycle graph with vertices vertices.
#cyclic_different
Generate a cyclic adjacency rule for a sequence.
#cyclic_latin_puzzle
Fill a cyclic Latin square.
#decision_step
Construct a decision record.
#default_model_limits
Practical limits for an embedded library use case.
#default_search_config
Construct the default deterministic configuration.
#demand_first_plan
Return a plan with customers ordered by descending demand.
#descending_value_config
Configuration that tries large values first.
#dispatch_schedule
Schedule operations with a deterministic earliest-feasible dispatch.
#distance
Require the absolute distance between two variables to equal distance.
#distinct_attempt_signatures
Return the number of distinct outcomes.
#distinct_route_vehicles
Return whether every route has a distinct vehicle identifier.
#domain
Create a domain containing every integer in [lower, upper].
#domain_add
Return all pairwise sums of two domains, if any pair exists.
#domain_array_signature
Return a stable domain-array signature.
#domain_candidate_count
Return the total number of candidate values.
#domain_change
Compute a domain change.
#domain_csv
Render domain intervals as a machine-readable string.
#domain_difference
Return values in left that do not occur in right, when non-empty.
#domain_entry
Create a domain entry.
#domain_from_values
Create a domain from an arbitrary set of values.
Values outside the smallest enclosing interval are not stored separately;
the interval is kept as the compact representation and the missing values
are recorded as holes. Duplicate values are ignored.
#domain_intersection
Return the intersection of two domains, or None if it is empty.
#domain_interval
Construct an interval.
#domain_over_degree_config
Configuration using domain-over-degree branching.
#domain_subtract
Return all pairwise differences of two domains, if any pair exists.
#domain_union
Return the union of two domains.
#due_date_order
Return operations ordered by earliest due date.
#edge_set_weight
Return the total weight of an edge set.
#eight_queens_solution
fn eight_queens_solution() -> String?Return a canonical placement for the eight-queens example.
#element
Constrain result to table[index].
#equal
Shorthand constructors keep models readable at the call site.
#equality_matrix
Return a stable pairwise equality matrix.
#equality_relation
Build an equality relation on a finite range.
#evaluate_scenario
Evaluate a scenario with a callback.
#evidence_pass_count
Return the number of passing records.
#evidence_record
fn evidence_record(name : String, metric : String, value : Int, expected : Int, passed : Bool) -> EvidenceRecordCreate an evidence record.
#evidence_summary
Return a stable evidence summary.
#exactly_k
Generate an exact-k Boolean cardinality encoding.
#exhaustive_search_config
Construct a configuration suitable for exhaustive enumeration.
#farthest_customer
Return the most distant customer from the depot.
#feasible_scenario_count
Return the number of feasible evaluations.
#feature_selection
Build an exactly-one selection model for a menu or feature toggle list.
#filled_matrix
Create a filled matrix.
#finite_relation
Create a relation.
#first_differences
Return first differences.
#first_fit_bin
Return the first bin with enough residual capacity.
#first_late_stop
Return the first route position violating a time window.
#first_solvable_n_queens
fn first_solvable_n_queens(lower : Int, upper : Int) -> Int?Return the smallest known solution size that has a non-empty placement.
#first_unassigned_config
Configuration using first-unassigned branching.
#flow_balance
Return supply imbalance at a vertex.
#flow_conserved
Return whether flow is conserved at all non-terminal vertices.
#flow_out
Return outgoing flow from a vertex.
#flow_shop
Return a two-machine flow-shop example.
#fm_abs
fn fm_abs(value : Int) -> IntExact integer mathematics used by finite-domain model builders.
#fm_interpolate
fn fm_interpolate(left : Int, right : Int, numerator : Int, denominator : Int) -> Int#free_slot_duration
Return total duration of free slots.
#free_slots
Return free slots.
#graph_coloring
Create a graph-coloring problem and post one inequality per edge.
#graph_edge
Construct an edge record.
#greedy_utility_subset
Select a greedy value-density subset.
#grid_coloring
Build a rectangular grid graph for map-coloring examples.
#grid_routing_points
Create a linearly spaced point set for deterministic examples.
#group_bounds
Generate lower and upper bound constraints for one variable group.
#heaviest_item
Return the heaviest assigned item in a bin.
#heuristic_markdown
Produce Markdown for comparative search output.
#heuristic_result
Construct a heuristic result.
#heuristics_agree
Return whether all compared strategies found the same number of solutions.
#improve_allocation
Improve bin balance through pair swaps.
#improve_allocation_once
Swap two items when it reduces the largest bin load.
#improve_two_opt
Apply the first improving 2-opt reversal found across all routes.
#improve_utility_subset
fn improve_utility_subset(items : Array[UtilityItem], selected : Array[Int], capacity : Int) -> Array[Int]Improve a subset through repeated swaps.
#increasing_chain
Generate a chain of strict inequalities.
#int_matrix_from_values
Create a matrix from row-major values.
#integer_correlation
Return correlation scaled by 1,000, or None when undefined.
#integer_covariance
Return a scaled covariance.
#integer_histogram
Create a histogram.
#integer_mean
Return the integer mean or zero for an empty sample.
#integer_mode
Return the mode, choosing the smallest value on ties.
#integer_objective
fn integer_objective(values : Array[Int], directions : Array[PortfolioDirection]) -> IntegerObjectiveCreate an objective vector.
#integer_outliers
Return values outside mean plus or minus k standard deviations.
#integer_percentile
Return a percentile using nearest-rank selection.
#integer_regression
Fit y = slope*x + intercept with slope scaled by 1,000.
#integer_set
Create a sorted set from arbitrary values.
#integer_set_range
Create a consecutive integer set.
#integer_sets_equal
Return whether two sets are equal.
#integer_stddev
Return population standard deviation as an integer.
#integer_summary
Summarize an integer sample.
#integer_tokens
Return all integer tokens in a document.
#integer_variance
Return population variance using integer division.
#interval_cover
Return a fixed cover model for the common interval-covering case.
#intervals_at
Return intervals that contain a time point.
#is_monotone_route
Return whether a route uses every stop in strictly increasing order.
#job_operations
Return operation ids for a job in process order.
#key_value
Find a key in key-value pairs.
#knapsack
Build a bounded 0/1 knapsack model.
#largest_service_order
Return the largest quantity observation.
#last_job_operation
Return the last operation of a job.
#late_jobs
Return jobs whose final operation is late.
#late_observations
Return late observations.
#late_project_tasks
Return tasks that finish after their due date.
#latest_service_order
Return the most late observation.
#latin_square
Build a Latin square over symbols 0..size-1.
#least_loaded_bin
Return the least loaded bin that can receive an item.
#less_than_relation
Build a less-than relation.
#less_than_table
Return a table for left < right over a finite value set.
#linear
Require a weighted sum to equal target.
#linear_forecast
Return a simple linear forecast from the last value and average delta.
#linear_greater_equal
Require a weighted sum to be at least target.
#linear_less_equal
Require a weighted sum to be at most target.
#lines_within_width
fn lines_within_width(input : String, width : Int) -> BoolReturn whether all lines have a maximum width.
#longest_assignment_run
Return the longest run of one label.
#loosest_resource
Return the resource with maximum idle time.
#machine_busy_time
fn machine_busy_time(instance : ManufacturingInstance, schedule : ManufacturingSchedule, machine : Int) -> IntReturn machine busy time.
#machine_operations
Return all operation ids for a machine in schedule order.
#machine_setup_transitions
fn machine_setup_transitions(instance : ManufacturingInstance, schedule : ManufacturingSchedule, machine : Int) -> IntReturn the number of setup transitions in a machine sequence.
#machine_spec
Create a machine specification.
#machine_utilization
fn machine_utilization(instance : ManufacturingInstance, schedule : ManufacturingSchedule, machine : Int) -> IntReturn machine utilization as an integer percentage.
#magic_square
Build a normal magic square model for a positive odd size.
#manufacturing_feasible
fn manufacturing_feasible(instance : ManufacturingInstance, schedule : ManufacturingSchedule) -> BoolReturn whether a schedule is valid.
#manufacturing_instance
fn manufacturing_instance(machines : Array[MachineSpec], operations : Array[ManufacturingOperation], setup_matrix : Array[Array[Int]]) -> ManufacturingInstance?Build an instance with a family-to-family setup matrix.
#manufacturing_jobs
Return all job identifiers in ascending order.
#manufacturing_makespan
fn manufacturing_makespan(instance : ManufacturingInstance, schedule : ManufacturingSchedule) -> IntReturn the makespan.
#manufacturing_objective
fn manufacturing_objective(instance : ManufacturingInstance, schedule : ManufacturingSchedule) -> IntReturn a scalar schedule objective.
#manufacturing_operation
fn manufacturing_operation(id : Int, job : Int, sequence : Int, machine : Int, setup : Int, duration : Int, family : Int, due : Int) -> ManufacturingOperationCreate an operation.
#manufacturing_render
fn manufacturing_render(instance : ManufacturingInstance, schedule : ManufacturingSchedule) -> StringRender a schedule as a stable Gantt summary.
#manufacturing_schedule
Create an empty schedule.
#manufacturing_setup_work
Return the total setup work.
#manufacturing_signature
fn manufacturing_signature(instance : ManufacturingInstance, schedule : ManufacturingSchedule) -> IntReturn a stable manufacturing fingerprint.
#manufacturing_tardiness
fn manufacturing_tardiness(instance : ManufacturingInstance, schedule : ManufacturingSchedule) -> IntReturn the total tardiness.
#manufacturing_work
Return the total processing work.
#matrix_all_different
Generate row and column constraints for a rectangular matrix.
#matrix_sum_constraints
fn matrix_sum_constraints(matrix : Array[Array[Int]], rows : Int, columns : Int, row_target : Int, column_target : Int) -> ConstraintSet?Generate a Latin-square line-sum system.
#maximize
Select maximization without constructing the enum directly.
#maximum
Bind result to the maximum value in variables.
#maximum_degree_config
Configuration favoring highly constrained variables.
#median_value_config
Configuration that tries the middle of a domain first.
#merge_calendar_windows
Merge overlapping and touching windows.
#merge_timeline_intervals
Merge adjacent intervals with the same resource and label.
#minimize
Select minimization without constructing the enum directly.
#minimum
Bind result to the minimum value in variables.
#minimum_capacity_slack
Return the smallest non-negative capacity residual, or -1 when infeasible.
#minimum_residual_capacity
Return the minimum residual capacity.
#missing_assignment_categories
Return category labels absent from a sequence.
#model_limits
Construct model limits with positive defaults for invalid values.
#model_report
Build a report from a solver.
#model_reports_signature
Return a report signature.
#most_expensive_report
Return the most expensive report.
#most_valuable_item
Return the most valuable assigned item in a bin.
#movable_task
Return the first task that can be moved later without breaking successors.
#moving_average
Return moving averages with a fixed window.
#n_queens
Build an N-Queens model using all-different columns and diagonal distance
constraints. The model is useful as a deterministic search benchmark.
#n_queens_with_first_column
Build a fixed-size N-Queens model with a preferred first row.
#nearest_neighbor_plan
Construct a deterministic nearest-neighbor seed plan.
#new_solver
Create an empty model using MRV and ascending value order.
#no_overlap
Require intervals (start variable, duration) not to overlap.
#non_attacking_diagonals
Generate non-attacking diagonal constraints for row-position variables.
#non_overlapping_intervals
Generate interval non-overlap constraints.
#nondecreasing_chain
Generate a chain of non-strict inequalities.
#nonogram_line
Create a nonogram line clue.
#nonogram_puzzle
Create a blank nonogram puzzle.
#normalize_objective
fn normalize_objective(value : Int, lower : Int, upper : Int) -> IntReturn a min/max normalized score.
#not_distance
Require two variables not to be at an exact absolute distance.
#not_equal
Require two variables to take different values.
#not_equal_table
Return a table for left != right over a finite value set.
#objective
Construct an objective from ordered terms.
#objective_from_directions
fn objective_from_directions(variables : Array[Int], directions : Array[OptimizationDirection]) -> Objective?Build a lexicographic objective from parallel arrays.
#objective_meets
Return whether an objective value meets a bound.
#objective_row_signature
Return an objective row signature.
#objective_term
Create an objective component with a non-zero weight.
#observations_from_times
fn observations_from_times(promised : Array[Int], actual : Array[Int], penalty : Int) -> Array[ServiceObservation]Create observations from promised and actual sequences.
#occupied_slots
Return slots occupied by any window.
#on_time_observations
Return on-time observations.
#one_hot
Generate a one-hot Boolean encoding.
#operation_finish
fn operation_finish(instance : ManufacturingInstance, schedule : ManufacturingSchedule, operation : Int) -> IntReturn operation finish time.
#operational_capacity_ok
#operational_delta
#operational_merge
#operational_next_state
#operational_record
fn operational_record(key : String, state : OperationalState, started_at : Int, finished_at : Int, attempts : Int, output_count : Int, error_code : Int) -> OperationalRecord#operational_record_repair
#operational_record_retryable
#operational_record_terminal
#operational_records_sorted_by_finish
fn operational_records_sorted_by_finish(records : Array[OperationalRecord]) -> Array[OperationalRecord]#operational_records_terminal
#operational_records_total_output
#operational_retry_state
#operational_score
fn operational_score(completion_percent : Int, violations : Int, penalty : Int) -> IntReturn an operational score from completion and violations.
#operational_state_equal
#operational_summary_balance
#operational_summary_report
#operational_window
fn operational_window(ledger : OperationalLedger, start : Int, finish : Int) -> Array[OperationalRecord]#operational_window_output
#overloaded_capacity_points
fn overloaded_capacity_points(project : ProjectPlan, starts : Array[Int]) -> Array[PlanningCapacityPoint]Return overloaded capacity points.
#overloaded_resources
Return overloaded resource ids.
#pairwise_assignment_changes
Return whether every adjacent pair is different.
#pareto_rows
Return nondominated rows.
#parse_csv_row
Parse one CSV row with quoted fields.
#parse_domain
Parse a finite domain from a comma-separated integer list.
#parse_edge_list
Parse a small edge list with one from,to,weight row per line.
#parse_int_matrix
Parse a rectangular integer CSV matrix.
#parse_integer_list
Parse a comma-separated integer list.
#parse_integer_sequence
Parse a whitespace-separated integer sequence.
#parse_key_values
Parse key-value pairs separated by =.
#plan_distance
Return a plan's total travel distance.
#plan_load
Return a plan's total load.
#plan_makespan
Return a plan's largest route distance.
#plan_objective
Return a plan-level objective with deterministic penalties.
#plan_visit_count
Return the number of customers served by a plan.
#planning_capacity_point
fn planning_capacity_point(resource : Int, time : Int, load : Int, capacity : Int) -> PlanningCapacityPointCreate a capacity point.
#planning_chain
fn planning_chain(count : Int, duration : Int, resource : Int, demand : Int, horizon : Int, capacity : Int) -> ProjectPlan?Construct a chain of equal-duration tasks.
#planning_dependency
Create a dependency with a minimum lag after the predecessor.
#planning_task
fn planning_task(id : Int, name : String, duration : Int, resource : Int, demand : Int) -> PlanningTaskCreate a task with no release restriction and an open due date.
#portfolio_audit
Summarize a portfolio.
#portfolio_direction
Choose the direction for a scalar metric.
#portfolio_objective_term
fn portfolio_objective_term(name : String, direction : PortfolioDirection, weight : Int) -> PortfolioObjectiveTermConstruct an objective term.
#post_absolute_table
Post an absolute-value relation.
#post_addition
Post a finite addition relation result = left + right.
#post_arithmetic_relation
Post a finite custom arithmetic relation.
#post_assignments
Add a sequence of fixed assignments to a model.
#post_at_most_one
Post at-most-one for Boolean indicator variables.
#post_binary_table
Post a binary relation table for two variables.
#post_binary_vector
Add a zero-or-one vector constraint.
#post_boolean_and
Post Boolean conjunction.
#post_boolean_equivalent
Post Boolean equivalence.
#post_boolean_implies
Post implication left -> right.
#post_boolean_not
Post a unary Boolean negation relation: result = !input.
#post_boolean_or
Post Boolean disjunction.
#post_boolean_xor
Post exclusive-or.
#post_chain_not_equal
Add a pairwise inequality chain to a model.
#post_clause
Post a disjunctive clause over positive or negative literals.
A literal is encoded as (variable, polarity), where polarity true
means the variable itself and false means its negation.
#post_division_table
Post an integer division relation for a non-zero divisor.
#post_exactly_k
Post an exactly-k cardinality constraint over Boolean indicators.
#post_exactly_one
Post exactly-one for Boolean indicator variables.
#post_indicator
Create a Boolean indicator for an exact value through a two-row table.
#post_maximum_table
Post a maximum relation for two input variables.
#post_minimum_table
Post a minimum relation for two input variables.
#post_modulo_table
Post a modulo relation for a positive divisor.
#post_one_hot
Add a one-hot vector constraint.
#post_pairwise_different
Add all pairwise inequality constraints for small models.
#post_product
Post a finite product relation.
#post_reified_equal
Post a reified equality: indicator=1 iff left=right.
#post_reified_less_than
Post a reified ordering: indicator=1 iff left<right.
#post_relation_pairs
Post any binary finite relation supplied by the caller.
#post_subtraction
Post a finite subtraction relation result = left - right.
Return all records with the best primary objective.
#project_capacity_points
fn project_capacity_points(project : ProjectPlan, starts : Array[Int]) -> Array[PlanningCapacityPoint]Return all project capacity points.
#project_dependency_count
Return the number of dependency arcs.
#project_plan
fn project_plan(tasks : Array[PlanningTask], horizon : Int, capacities : Array[Int]) -> ProjectPlan?Construct a project plan and its start variables.
#project_planning_report
Return a compact planning report.
#project_predecessor_count
Return the total number of predecessor relationships.
#project_schedule_score
Return a project schedule score.
#project_tardiness
Return total tardiness.
#pruning_explanation
Create an empty explanation.
#pruning_reason
fn pruning_reason(variable : Int, value : Int, constraint : String, detail : String) -> PruningReasonCreate a pruning reason.
#quality_gate_result
fn quality_gate_result(name : String, status : QualityStatus, observed : Int, threshold : Int, detail : String) -> QualityGateResultConstruct a result.
#queue_length_trace
Return the maximum queue length observed in a trace.
#queue_simulation
Create a queue simulation and seed arrivals.
#quota_sequence
fn quota_sequence(length : Int, lower : Int, upper : Int, total : Int, maximum_run : Int) -> SequenceModel?Build a bounded sequence with a fixed total and no long runs.
#rebalance
Apply local balancing until no improving move remains.
#rebalance_once
Move one item from a heavier bin to a lighter feasible bin.
#recipe_balanced_binary
fn recipe_balanced_binary(recipe : ConstraintRecipe, name : String, length : Int, ones : Int) -> Array[Int]Add a balanced binary vector.
#recipe_coloring
fn recipe_coloring(recipe : ConstraintRecipe, name : String, vertices : Int, colors : Int, edges : Array[(Int, Int)]) -> Array[Int]Add a graph coloring vector.
#recipe_coverage
fn recipe_coverage(recipe : ConstraintRecipe, name : String, slots : Int, workers : Int, per_worker : Int) -> Array[Int]Add a worker coverage vector.
#recipe_interval_chain
fn recipe_interval_chain(recipe : ConstraintRecipe, name : String, count : Int, horizon : Int, duration : Int) -> Array[Int]Add an interval chain.
#recipe_knapsack
fn recipe_knapsack(recipe : ConstraintRecipe, name : String, weights : Array[Int], capacity : Int) -> Array[Int]Add a bounded knapsack selection vector.
#recipe_latin
Add a Latin square.
#recipe_magic_square
Add a magic-square sum model.
#recipe_permutation
Add a finite-domain permutation.
#recipe_resource_chain
fn recipe_resource_chain(recipe : ConstraintRecipe, name : String, count : Int, horizon : Int, duration : Int, height : Int, capacity : Int) -> Array[Int]Add a cumulative resource chain.
#recipe_rotating_roster
fn recipe_rotating_roster(recipe : ConstraintRecipe, name : String, slots : Int, workers : Int) -> Array[Int]Add a roster with no adjacent repeated worker.
#recipe_sequence
fn recipe_sequence(recipe : ConstraintRecipe, name : String, length : Int, lower : Int, upper : Int) -> Array[Int]Add a bounded sequence with adjacent changes.
#recommended_capacity
Return a capacity recommendation for a target utilization percentage.
#rectangle_sum
Query an inclusive rectangle sum using a prefix matrix.
#redact_key_values
Redact values after configured key prefixes.
#regressed
fn regressed(baseline : Int, candidate : Int, tolerance_percent : Int) -> BoolReturn whether a metric regressed beyond a tolerance percentage.
#relation_pair
Construct a relation pair.
#relation_pairs
Return all finite pairs that satisfy a predicate.
#relation_table
Build a relation table from rows with a common arity.
#render_csv_row
Render a CSV row with quote escaping.
#render_integer_sequence
Render integer values with stable spacing.
#render_model_reports
Render reports as a markdown table.
#repair_capacity
Move one customer from an overloaded route to the best insertion point.
#repeat_benchmark
Aggregate counters across repeated deterministic runs.
#replace_text
fn replace_text(input : String, target : String, replacement : String) -> StringReplace all occurrences of a substring.
#report_within_tolerance
fn report_within_tolerance(baseline : ModelReport, candidate : ModelReport, tolerance_percent : Int) -> BoolReturn whether the candidate is within a work tolerance.
#report_work_change_percent
Return a regression ratio in integer percentage points.
#residual_reachable
Return vertices reachable from source in the residual network.
#resource_balance_score
Return a scaled load balance score.
#resource_metric
Build a metric from a profile.
#resource_metrics_report
Return a report of metrics.
#resource_metrics_signature
Return a stable metrics fingerprint.
#resource_scenario
Return a small resource scheduling scenario.
#resource_schedule
Create an empty resource schedule.
#resource_timeline
Create an empty timeline.
#review_project
Check a project schedule.
#review_routing
Check a route plan.
#review_solver
Check a solver model and optional solution.
#review_timeline
Check a resource timeline.
#risk_item
Create a risk item with values in 0..100.
#risk_meets_target
Return whether residual risk meets a target.
#risk_priority
Return the risk-weighted mitigation priority.
#risk_scenario
Create a risk scenario.
#risk_sensitivity
Return sensitivity of residual risk to a likelihood change.
#rolling_majority
Return a rolling majority label.
#roster_model
Build an unconstrained roster with one shift variable per worker/day.
#rotating_roster
Build a small rotating-shift roster template.
#rotating_schedule
Return a canonical small rotating schedule used by docs and benchmarks.
#round_robin_plan
Return a plan created by assigning each customer round-robin.
#route_arrivals
Arrival and departure times for a route under its time windows.
#route_crossings
Return the number of route crossings in a coordinate instance.
#route_distance
Route distance including the depot departure and return.
#route_load
Sum customer demand on a route.
#route_objective
Return a route-level objective combining distance, lateness, and crossings.
#route_service
Return the route's service time excluding travel.
#route_slacks
Return a route's cumulative arrival slack values.
#routing_ids_are_dense
Return whether every point identifier matches its array position.
#routing_instance
fn routing_instance(points : Array[RoutingPoint], depot : Int, capacities : Array[Int]) -> RoutingInstance?Build a routing instance from customer points and vehicle capacities.
#routing_instance_with_distances
fn routing_instance_with_distances(points : Array[RoutingPoint], depot : Int, capacities : Array[Int], distances : Array[Array[Int]]) -> RoutingInstance?Build a square instance with an explicit distance matrix.
#routing_matrix
Return a plan's visit sequence grouped by vehicle.
#routing_plan
Create an empty plan with one route per vehicle.
#routing_point
Create a customer or depot point with a wide time window.
#routing_report
Produce a report without mutating the plan.
#routing_score
A scalar score useful for comparing heuristic plans.
#routing_signature
Return an integer route signature for caching and regression tests.
#same_int_array
Return whether two integer assignments are equal.
#same_project_order
Return whether two project schedules have the same precedence order.
#scenario
Construct a scenario record.
#scenario_axis
Create an axis.
#scenario_evaluation_signature
Return a stable evaluation fingerprint.
#scenario_product
Generate all combinations up to a hard limit.
#scenario_product_size
Return the number of combinations, capped at a limit.
#scenario_report
fn scenario_report(limit : Int) -> StringRun every smoke scenario and return a concise report.
#scenario_score_spread
Return score spread among evaluations.
#scenario_suite
Return a canonical model scenario collection.
#scenario_value
Create a named scenario value.
#scenarios_pass
fn scenarios_pass(limit : Int) -> BoolReturn whether every smoke scenario meets its expected count.
#schedule_from_pattern
Build a schedule from a fixed pattern and verify its dimensions.
#schedule_preference
fn schedule_preference(day : Int, shift : Int, preferred_person : Int, penalty : Int) -> SchedulePreferenceConstruct a preference record.
#schedule_scenario
Return a schedule scenario with rotation rules.
#schema_field
Create a field.
#schemas_compatible
Return whether two schemas have the same field shape.
#score_preferences
Return a score for a schedule preference set.
#score_solution
Compute objective scores from a solution using variable indices.
#scored_solution
Build a scored solution.
#sequence_checksum
Compute a deterministic checksum.
#sequence_contains
Return whether a sequence contains a contiguous pattern.
#sequence_convolve
Return a weighted convolution with a same-length output.
#sequence_cumulative
Return a cumulative sum sequence.
#sequence_cyclic_alternates
Return whether a sequence is cyclically alternating.
#sequence_delta
Return a first-difference sequence with an initial zero.
#sequence_edit_distance
Return Levenshtein edit distance.
#sequence_lcs_length
Return the longest common subsequence length.
#sequence_model
Build a sequence of length variables in the inclusive value interval.
#sequence_normalize
Normalize values by subtracting the minimum.
#sequence_report
Analyze an integer sequence.
#sequence_run_lengths
Return run lengths.
#sequence_runs
Return run values paired with their lengths.
#sequence_summary
Compute summary statistics for an array of values.
#service_gap
Return a service-level gap.
#service_ids_dense
Return whether ids are dense.
#service_ids_ordered
Return whether all observations are ordered by id.
#service_lateness_percentile
Return the lateness percentile.
#service_lateness_values
Return a sorted lateness vector.
#service_metrics
Aggregate observations.
#service_metrics_signature
Return a stable aggregate signature.
#service_observation
fn service_observation(id : Int, promised : Int, actual : Int, quantity : Int, penalty : Int) -> ServiceObservationCreate a service observation.
#service_score
fn service_score(observations : Array[ServiceObservation], lateness_weight : Int, quantity_weight : Int) -> IntReturn weighted service score.
#set_covering
fn set_covering(universe_size : Int, covers : Array[Array[Int]], costs : Array[Int]) -> SetCovering?Build a covering problem. Each row lists the universe elements covered by
one candidate set; every universe element must be covered at least once.
#setup_time
fn setup_time(instance : ManufacturingInstance, machine : Int, from_family : Int, to_family : Int) -> IntReturn setup time between two operation families on a machine.
#shift_project_schedule
Shift a schedule by a constant offset.
#shift_timeline
Shift all intervals by an offset.
#simulation_entity
Create an entity.
#simulation_event
fn simulation_event(time : Int, sequence : Int, entity : Int, resource : Int, kind : SimulationEventKind, payload : Int) -> SimulationEventConstruct an event.
#single_objective
Construct a one-variable objective.
#singleton_domain
Create a domain containing one value.
#singleton_domain_ids
Return variables with singleton domains.
#snapshot_assume
Apply assumptions to a snapshot and return the resulting state.
#snapshot_assumptions
Return all fixed values in a snapshot as variable/value assumptions.
#snapshot_deltas
Compute changed domains between two snapshots.
#snapshots_equal
Compare two snapshots by their available values.
#solution_delta
Return all changed positions between two solutions.
#solution_delta_report
Return a solution delta report.
#solution_distance
Count positions that differ between two solutions.
#solution_pool
Create an empty portfolio pool.
#solution_record
Build a record from a solution and score vector.
#solution_replace
Return a copied solution with one variable changed.
#solution_signature
Return a solution fingerprint.
#solution_values
Return a solution as a copied integer array.
#solutions_csv
Render a set of solutions as newline-separated CSV.
#solutions_equal
Compare two solutions by all variable values.
#solve_attempt
fn solve_attempt(index : Int, solved : Bool, solution_count : Int, stats : SearchStats, signature : Int) -> SolveAttemptCreate an attempt record.
#solved_report_count
Return solved report count.
#solver_domain_changes
Return a change list between two solvers.
#solver_domains
Return a domain snapshot from a solver.
#solver_validation_report
Return a report from a solver solve attempt.
#sort_attempts_by_work
Return attempts ordered by work.
#sort_integers
Sort an integer array in ascending order.
#sort_scenario_evaluations
fn sort_scenario_evaluations(evaluations : Array[ScenarioEvaluation], minimize : Bool) -> Array[ScenarioEvaluation]Return the feasible evaluations ordered by score.
#source_scale_evidence
Build source-scale evidence.
#speedup_percent
fn speedup_percent(baseline : Int, candidate : Int) -> Int?Return a benchmark speedup percentage.
#strong_all_different
Generate an all-different global constraint and a redundant pairwise set.
#subset_sum
Solve a subset-sum instance using Boolean selection variables.
#sudoku
Parse a Sudoku puzzle. Digits 1-9 are givens; 0 and . are blanks.
Whitespace is ignored so puzzles can be copied from formatted grids.
#sudoku_from_grid
Construct a Sudoku from a 9x9 matrix, returning None for malformed data.
#sudoku_grid
Convert a compact board string into a 9x9 integer matrix.
#sum
Require the listed variables to add up to target.
#sum_table
Return a table for a finite arithmetic sum.
#table
Restrict a tuple of variables to rows in an extensional table.
#tasks_by_slack
Return tasks sorted by slack.
#text_document
Split a document into owned lines.
#text_token
Create a token.
#tightest_capacity_point
Return the tightest capacity point.
#tightest_resource
Return the resource with minimum idle time.
#timeline_interval
fn timeline_interval(id : Int, resource : Int, start : Int, end : Int, weight : Int, label : String) -> TimelineIntervalCreate an interval.
#timeline_objective
Return a timeline quality score.
#timeline_overlap
Return interval overlap.
#timeline_resource_metrics
Build metrics for every resource of a timeline.
#tokenize_text
Tokenize non-whitespace runs while preserving locations.
#total_actual
Return total actual time.
#total_allocated_value
Return total assigned value.
#total_allocated_weight
Return total assigned weight.
#total_promised
Return total promised time.
#total_resource_capacity
Return total capacity across metrics.
#total_resource_load
Return total load across metrics.
#trace_is_ordered
Return whether every event timestamp is nondecreasing.
#trace_solution
Create a trace from a complete solution and its variable ids.
#two_shift_roster
Build a two-shift roster with a minimum number of workers on each shift.
#two_way_partition
Build a partition model with two groups and an exact target sum.
#unassigned_items
Return unassigned item ids.
#union_domain_values
Return the union of domain candidates.
#unserved_count
Return the number of unserved customers.
#utility_best_swap
fn utility_best_swap(items : Array[UtilityItem], selected : Array[Int], capacity : Int) -> (Array[Int], Bool)Return the best single-item replacement for a selected subset.
#utility_item
Create a utility item.
#utility_subset_value
Return total value of selected items.
#utility_subset_weight
Return total weight of selected items.
#validate_allocation
Return a stable validation error list.
#validate_assignment_domains
fn validate_assignment_domains(report : ValidationReport, domains : Array[Domain], values : Array[Int]) -> BoolValidate every assignment fits its variable domain.
#validate_domain_table
Validate a rectangular domain table.
#validate_domains
Validate all domains are non-empty.
#validate_increasing
Validate an array is strictly increasing.
#validate_length
fn validate_length(report : ValidationReport, path : String, values : Array[Int], expected : Int) -> BoolValidate an array's exact length.
#validate_manufacturing_schedule
fn validate_manufacturing_schedule(instance : ManufacturingInstance, schedule : ManufacturingSchedule) -> Array[String]Validate precedence, machine order, and due dates.
#validate_matrix_shape
fn validate_matrix_shape(report : ValidationReport, path : String, matrix : Array[Array[Int]], rows : Int, columns : Int) -> BoolValidate matrix shape.
#validate_membership
fn validate_membership(report : ValidationReport, path : String, values : Array[Int], allowed : Array[Int]) -> BoolValidate every value belongs to a finite set.
#validate_model
Validate a finite-domain model before solving.
#validate_names
Validate a set of names is unique and non-empty.
#validate_nondecreasing
Validate an array is nondecreasing.
#validate_nonnegative
Validate a non-negative integer.
#validate_plan
Validate that every customer appears exactly once.
#validate_range
fn validate_range(report : ValidationReport, path : String, value : Int, lower : Int, upper : Int) -> BoolValidate an inclusive integer range.
#validate_route
Validate a single route and return a stable error code.
#validate_routing_report
Validate route errors into a structured report.
#validate_solution
Validate a solver solution against its variable declarations.
#validate_text
Validate a text field is not blank.
#validate_unique
Validate uniqueness.
#validate_utility_subset
fn validate_utility_subset(items : Array[UtilityItem], selected : Array[Int], capacity : Int) -> BoolValidate a selected subset.
#validation_issue
fn validation_issue(code : String, path : String, message : String, severity : ValidationSeverity) -> ValidationIssueConstruct an issue.
#value_density_plan
Allocate items in descending value-density order.
#variable
Define a variable with an inclusive integer range.
#variable_group
Create a group from identifiers.
#variable_with_domain
Define a variable from an explicit finite domain.
#vehicle_route
Create an empty route for a vehicle.
#vehicle_route_from
Create a route from a stop sequence.
#weighted_graph
Create an empty graph.
#weighted_graph_from_edges
fn weighted_graph_from_edges(vertices : Int, directed : Bool, edges : Array[GraphEdge]) -> WeightedGraph?Create a graph from an edge list.
#weighted_score
Return a scaled weighted score.
#weighted_sum_bundle
Generate a weighted equality and both safe bound relaxations.
#what_if_scenario
Create a scenario.
#within_resource_limit
Return whether all resources stay below a utilization limit.
#word_frequency
Return word frequencies in first-seen order.
Source Files
- allocation_models.mbt
- assignment_analytics.mbt
- benchmark_artifacts.mbt
- benchmarks.mbt
- boolean_constraints.mbt
- builder.mbt
- calendar_metrics_extra.mbt
- calendar_templates.mbt
- constraint_combinators.mbt
- constraint_extensions.mbt
- constraint_recipes.mbt
- diagnostics.mbt
- domain.mbt
- domain_algorithms.mbt
- domain_catalog.mbt
- domain_explain.mbt
- event_simulation.mbt
- evidence_utils.mbt
- finite_model_math.mbt
- flow_models.mbt
- graph_algorithms.mbt
- graph_coloring.mbt
- incremental.mbt
- integer_tables.mbt
- knapsack.mbt
- manufacturing.mbt
- matrix_models.mbt
- model.mbt
- model_execution.mbt
- model_guardrails.mbt
- model_io.mbt
- model_reports.mbt
- nqueens.mbt
- operational_ledger.mbt
- optimization.mbt
- optimization_utilities.mbt
- planner_utils.mbt
- problem_library.mbt
- project_planning.mbt
- puzzle_models.mbt
- quality_gates.mbt
- resource_metrics.mbt
- resource_schedule.mbt
- result_utils.mbt
- risk_models.mbt
- roster.mbt
- routing.mbt
- scenario_engine.mbt
- scenario_library.mbt
- schedule.mbt
- schema_tools.mbt
- search_analysis.mbt
- search_portfolio.mbt
- search_types.mbt
- sequence_analytics.mbt
- sequence_models.mbt
- service_metrics.mbt
- set_models.mbt
- solver.mbt
- solver_metrics.mbt
- statistics.mbt
- sudoku.mbt
- table_models.mbt
- text_models.mbt
- timeline_analytics.mbt
- validation_tools.mbt
Finite-domain constraint programming library for MoonBit
constraint-programmingfinite-domainschedulingoptimization
0.1.0
Apache-2.0
Updated 7 hours ago
2 downloads