Crate highs

Safe rust binding to the HiGHS linear programming solver.

Usage example

Building a problem constraint by constraint

Useful for traditional problem modelling where you first declare your variables, then add constraints one by one.

use highs::{Sense, Model, HighsModelStatus, RowProblem};
// max: x + 2y + z
// under constraints:
// c1: 3x +  y      <= 6
// c2:       y + 2z <= 7
let mut pb = RowProblem::default();
let x = pb.add_column(1., 0..);
let y = pb.add_column(2., 0..);
let z = pb.add_column(1., 0..);
// c1
pb.add_row(..=6, &[(x, 3.), (y, 1.)]);
// c2
pb.add_row(..=7, &[(y, 1.), (z, 2.)]);

let solved = pb.optimise(Sense::Maximise).solve();

assert_eq!(solved.status(), HighsModelStatus::Optimal);

let solution = solved.get_solution();
// The expected solution is x=0  y=6  z=0.5
assert_eq!(solution.columns(), vec![0., 6., 0.5]);
// All the constraints are at their maximum
assert_eq!(solution.rows(), vec![6., 7.]);

Building a problem variable by variable

Useful for resource allocation problems and other problems when you know in advance the number of constraints and their bounds, but dynamically add new variables to the problem.

use highs::{Sense, Model, HighsModelStatus, ColProblem};
// max: x + 2y + z
// under constraints:
// c1: 3x +  y      <= 6
// c2:       y + 2z <= 7
let mut pb = ColProblem::default();
let c1 = pb.add_row(..6.);
let c2 = pb.add_row(..7.);
// x
pb.add_column(1., 0.., &[(c1, 3.)]);
// y
pb.add_column(2., 0.., &[(c1, 1.), (c2, 1.)]);
// z
pb.add_column(1., 0.., vec![(c2, 2.)]);

let solved = pb.optimise(Sense::Maximise).solve();

assert_eq!(solved.status(), HighsModelStatus::Optimal);

let solution = solved.get_solution();
// The expected solution is x=0  y=6  z=0.5
assert_eq!(solution.columns(), vec![0., 6., 0.5]);
// All the constraints are at their maximum
assert_eq!(solution.rows(), vec![6., 7.]);

Structs

Col	Represents a variable
ColMatrix	A constraint matrix to build column-by-column
Model	A model to solve
Problem	A complete optimization problem Depending on the MATRIX type parameter, the problem will be built constraint by constraint (with MATRIX=RowMatrix), or variable by variable (with MATRIX=ColMatrix)
Row	Represents a constraint
RowMatrix	A complete optimization problem stored by row
Solution	Concrete values of the solution
SolvedModel	A solved model

Enums

HighsModelStatus	The kinds of results of an optimization
HighsStatus	The status of a highs operation
Sense	Whether to maximize or minimize the objective function

Type Definitions

ColProblem	A problem where constraints are declared first, and variables are then added dynamically.
RowProblem	A problem where variables are declared first, and constraints are then added dynamically.