1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121
/*! Linear programming library that provides primal and dual simplex solvers. Both solvers are currently working for a small set of test problems. This library is an *early work-in-progress*. ## Examples Here is example code that sets up a linear program, and then solves it with both the primal and dual simplex solvers. First we setup the problem ``` use ellp::*; let mut prob = Problem::new(); let x1 = prob .add_var(2., Bound::TwoSided(-1., 1.), Some("x1".to_string())) .unwrap(); let x2 = prob .add_var(10., Bound::Upper(6.), Some("x2".to_string())) .unwrap(); let x3 = prob .add_var(0., Bound::Lower(0.), Some("x3".to_string())) .unwrap(); let x4 = prob .add_var(1., Bound::Fixed(0.), Some("x4".to_string())) .unwrap(); let x5 = prob .add_var(0., Bound::Free, Some("x5".to_string())) .unwrap(); prob.add_constraint(vec![(x1, 2.5), (x2, 3.5)], ConstraintOp::Gte, 5.) .unwrap(); prob.add_constraint(vec![(x2, 2.5), (x1, 4.5)], ConstraintOp::Lte, 1.) .unwrap(); prob.add_constraint(vec![(x3, -1.), (x4, -3.), (x5, -4.)], ConstraintOp::Eq, 2.) .unwrap(); println!("{}", prob); let primal_solver = PrimalSimplexSolver::default(); let dual_solver = DualSimplexSolver::default(); let primal_result = primal_solver.solve(prob.clone()).unwrap(); let dual_result = dual_solver.solve(prob).unwrap(); if let SolverResult::Optimal(sol) = primal_result { println!("primal obj: {}", sol.obj()); println!("primal opt point: {}", sol.x()); } else { panic!("should have an optimal point"); } if let SolverResult::Optimal(sol) = dual_result { println!("dual obj: {}", sol.obj()); println!("dual opt point: {}", sol.x()); } else { panic!("should have an optimal point"); } ``` The output is ```console minimize + 2 x1 + 10 x2 + 1 x4 subject to + 2.5 x1 + 3.5 x2 ≥ 5 + 2.5 x2 + 4.5 x1 ≤ 1 - 1 x3 - 3 x4 - 4 x5 = 2 with the bounds -1 ≤ x1 ≤ 1 x2 ≤ 6 x3 ≥ 0 x4 = 0 x5 free primal obj: 19.157894736842103 primal opt point: ┌ ┐ │ -0.9473684210526313 │ │ 2.1052631578947367 │ │ 0 │ │ 0 │ │ -0.5 │ └ ┘ dual obj: 19.157894736842103 dual opt point: ┌ ┐ │ -0.9473684210526313 │ │ 2.1052631578947367 │ │ 0 │ │ 0 │ │ -0.5 │ └ ┘ ``` If the problem is infeasible or unbounded, then `solve` will return [`SolverResult::Infeasible`] or [`SolverResult::Unbounded`], respectively. */ mod dual; mod error; mod primal; pub mod problem; pub mod solver; mod standard_form; mod util; pub use crate::dual::dual_simplex_solver::DualSimplexSolver; pub use crate::error::EllPError; pub use crate::primal::primal_simplex_solver::PrimalSimplexSolver; pub use crate::problem::{Bound, Constraint, ConstraintOp, Problem, Variable}; pub use crate::solver::{EllPResult, SolverResult};