Crate highs[−][src]
Safe rust binding to the HiGHS linear programming solver.
Usage example
Building a problem constraint by constraint with RowProblem
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(); // Create a variable named x, with a coefficient of 1 in the objective function, // that is bound between 0 and +∞. let x = pb.add_column(1., 0..); let y = pb.add_column(2., 0..); let z = pb.add_column(1., 0..); // constraint c1: x*3 + y*1 is bound to ]-∞; 6] pb.add_row(..=6, &[(x, 3.), (y, 1.)]); // constraint c2: y*1 + z*2 is bound to ]-∞; 7] 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 with ColProblem
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.
This is slightly more efficient than building the problem constraint by constraint.
use highs::{ColProblem, Sense}; let mut pb = ColProblem::new(); // We cannot use more then 5 units of sugar in total. let sugar = pb.add_row(..=5); // We cannot use more then 3 units of milk in total. let milk = pb.add_row(..=3); // We have a first cake that we can sell for 2€. Baking it requires 1 unit of milk and 2 of sugar. pb.add_column(2., 0.., &[(sugar, 2.), (milk, 1.)]); // We have a second cake that we can sell for 8€. Baking it requires 2 units of milk and 3 of sugar. pb.add_column(8., 0.., &[(sugar, 3.), (milk, 2.)]); // Find the maximal possible profit let solution = pb.optimise(Sense::Maximise).solve().get_solution(); // The solution is to bake only 1.5 portions of the second cake assert_eq!(solution.columns(), vec![0.,1.5]);
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 |
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.
See |
RowProblem | A problem where variables are declared first, and constraints are then added dynamically.
See |