Freon793/moonopt/core - mooncakes.io

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Solution
- ::defaultSolveOptions::default
- ::newSolveOptions::new
- ::with_max_nodesSolveOptions::with_max_nodes
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SolveStatus
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relaxation
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solve
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solve_with

Freon793/moonopt/core does not have a README file

CompensatedSum

pub struct CompensatedSum {
  total : Double
  correction : Double
}

Neumaier compensated summation.

Plain += loses low order bits when a large accumulator receives many small terms, which is exactly the pattern of a simplex pivot row. The compensated form keeps a second accumulator holding the rounding error.

CompensatedSum::add

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fn CompensatedSum::add(self : CompensatedSum, x : Double) -> Unit

Adds one term, preserving the bits that a plain addition would drop.

CompensatedSum::new

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fn CompensatedSum::new() -> CompensatedSum

Creates an empty accumulator.

CompensatedSum::value

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fn CompensatedSum::value(self : CompensatedSum) -> Double

Current compensated value of the accumulated terms.

SparseMatrix

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pub struct SparseMatrix {
  nrows : Int
  ncols : Int
  col_ptr : Array[Int]
  row_idx : Array[Int]
  values : Array[Double]
} derive(@moonbitlang/core/debug.Debug
Debug)

A sparse matrix in compressed sparse column form.

SparseMatrix::col_nnz

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fn SparseMatrix::col_nnz(self : SparseMatrix, j : Int) -> Int

Number of stored entries in column j.

SparseMatrix::col_pointers

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fn SparseMatrix::col_pointers(self : SparseMatrix) -> ArrayView[Int]

Raw column pointers, for kernels whose inner loops must not allocate.

With row_indices and values_view this exposes the storage so a solver can walk the entries of a column in place: column(j) builds a fresh (row, value) list per call, which is convenient but costs an allocation per entry per pivot in a hot loop. The invariants documented at the top of this file still hold for everything reached this way; the views must not outlive a call that rebuilds the matrix.

SparseMatrix::column

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fn SparseMatrix::column(self : SparseMatrix, j : Int) -> Array[(Int, Double)]

(row, value) pairs of column j, row index ascending.

SparseMatrix::from_columns

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fn SparseMatrix::from_columns(nrows : Int, columns : Array[Array[(Int, Double)]]) -> SparseMatrix

Builds a matrix from per-column (row, value) pairs.

Entries are sorted by row index, duplicate rows inside a column are summed, and exact zeros are dropped. Passing a row index outside [0, nrows) is a programming error and aborts.

SparseMatrix::get

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fn SparseMatrix::get(self : SparseMatrix, i : Int, j : Int) -> Double

Entry at (i, j); missing entries read as 0.0.

SparseMatrix::mul_vec

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fn SparseMatrix::mul_vec(self : SparseMatrix, x : Array[Double]) -> Array[Double]

Matrix–vector product self * x.

SparseMatrix::ncols

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fn SparseMatrix::ncols(self : SparseMatrix) -> Int

Number of columns.

SparseMatrix::nnz

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fn SparseMatrix::nnz(self : SparseMatrix) -> Int

Number of stored (non-zero) entries.

SparseMatrix::nrows

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fn SparseMatrix::nrows(self : SparseMatrix) -> Int

Number of rows.

SparseMatrix::row_indices

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fn SparseMatrix::row_indices(self : SparseMatrix) -> ArrayView[Int]

Raw row indices of the stored entries.

SparseMatrix::to_dense

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fn SparseMatrix::to_dense(self : SparseMatrix) -> Array[Array[Double]]

Row-major dense copy. Intended for tests and for the reference solver, not for production paths.

SparseMatrix::transpose

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fn SparseMatrix::transpose(self : SparseMatrix) -> SparseMatrix

Transposes the matrix. Row i of self becomes column i of the result, so the result has ncols rows and nrows columns.

SparseMatrix::values_view

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fn SparseMatrix::values_view(self : SparseMatrix) -> ArrayView[Double]

Raw values of the stored entries, aligned with row_indices.

SparseMatrix::zeros

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fn SparseMatrix::zeros(nrows : Int, ncols : Int) -> SparseMatrix

Builds an all-zero matrix with the given shape.

approx_eq

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fn approx_eq(a : Double, b : Double, rel? : Double, abs? : Double) -> Bool

Scale-aware equality test with an explicit tolerance.

approx_negative

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fn approx_negative(x : Double, abs? : Double) -> Bool

Returns true when x counts as strictly negative at the given tolerance.

approx_positive

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fn approx_positive(x : Double, abs? : Double) -> Bool

Returns true when x counts as strictly positive at the given tolerance.

approx_zero

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fn approx_zero(x : Double, abs? : Double) -> Bool

Returns true when x counts as zero at the given absolute tolerance.

default_abs_tolerance

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let default_abs_tolerance : Double

Absolute part of the default comparison tolerance.

default_rel_tolerance

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let default_rel_tolerance : Double

Relative part of the default comparison tolerance.

dot_compensated

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fn dot_compensated(xs : Array[Double], ys : Array[Double]) -> Double

Dot product with compensation. The second argument is guarded: a shorter ys simply contributes nothing for the missing tail.

fabs

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fn fabs(x : Double) -> Double

Absolute value of a Double without depending on the standard library float helpers, so this package stays dependency free.

fmax

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fn fmax(a : Double, b : Double) -> Double

Larger of two Double values.

fmax_int

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fn fmax_int(a : Int, b : Int) -> Int

Larger of two integers.

fmin

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fn fmin(a : Double, b : Double) -> Double

Smaller of two Double values.

fmin_int

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fn fmin_int(a : Int, b : Int) -> Int

Smaller of two integers (kept local to stay dependency free).

is_finite

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fn is_finite(x : Double) -> Bool

Returns true when x is neither NaN nor an infinity.

x - x is 0.0 for every finite value and NaN for ±inf and NaN.

is_nan

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fn is_nan(x : Double) -> Bool

Returns true when x is NaN.

A NaN is the only value that is not equal to itself.

sum_compensated

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fn sum_compensated(xs : Array[Double]) -> Double

Sums an array with compensation.

Source Files

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Optimization kernels for MoonBit: MPS/LP model interop, a sparse revised simplex with presolve and a dual simplex warm start, branch and bound for models with integer variables, and independently checkable optimality, infeasibility and unboundedness certificates.

optimizationlinear-programmingmixed-integer-programmingsimplexmps

Freon793

Apache-2.0

github.com/Freon793/moonopt

Updated 5 days ago

80 downloads

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#CompensatedSum

#CompensatedSum::add

#CompensatedSum::new

#CompensatedSum::value

#SparseMatrix

#SparseMatrix::col_nnz

#SparseMatrix::col_pointers

#SparseMatrix::column

#SparseMatrix::from_columns

#SparseMatrix::get

#SparseMatrix::mul_vec

#SparseMatrix::ncols

#SparseMatrix::nnz

#SparseMatrix::nrows

#SparseMatrix::row_indices

#SparseMatrix::to_dense

#SparseMatrix::transpose

#SparseMatrix::values_view

#SparseMatrix::zeros

#approx_eq

#approx_negative

#approx_positive

#approx_zero

#default_abs_tolerance

#default_rel_tolerance

#dot_compensated

#fabs

#fmax

#fmax_int

#fmin

#fmin_int

#is_finite

#is_nan

#sum_compensated

Source Files

CompensatedSum

CompensatedSum::add

CompensatedSum::new

CompensatedSum::value

SparseMatrix

SparseMatrix::col_nnz

SparseMatrix::col_pointers

SparseMatrix::column

SparseMatrix::from_columns

SparseMatrix::get

SparseMatrix::mul_vec

SparseMatrix::ncols

SparseMatrix::nnz

SparseMatrix::nrows

SparseMatrix::row_indices

SparseMatrix::to_dense

SparseMatrix::transpose

SparseMatrix::values_view

SparseMatrix::zeros

approx_eq

approx_negative

approx_positive

approx_zero

default_abs_tolerance

default_rel_tolerance

dot_compensated

fabs

fmax

fmax_int

fmin

fmin_int

is_finite

is_nan

sum_compensated