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https://github.com/kehlert/ellp
Linear programming library (written in Rust) that provides primal and dual simplex solvers.
https://github.com/kehlert/ellp
Last synced: about 2 months ago
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Linear programming library (written in Rust) that provides primal and dual simplex solvers.
- Host: GitHub
- URL: https://github.com/kehlert/ellp
- Owner: kehlert
- License: mit
- Created: 2021-05-18T21:52:44.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-12-26T01:06:52.000Z (over 1 year ago)
- Last Synced: 2024-05-09T07:32:52.587Z (about 2 months ago)
- Language: Rust
- Homepage:
- Size: 128 KB
- Stars: 4
- Watchers: 3
- Forks: 0
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Lists
- awesome-rust-formalized-reasoning - ellp - linear programming library that provides primal and dual simplex solvers. (Projects / Provers and Solvers)
README
# ellp
[](https://crates.io/crates/ellp)
[](https://docs.rs/ellp/)
[](https://github.com/kehlert/ellp/blob/dev/LICENSE.txt)
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.
```rust
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
```
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.
## Development priorities
* clean up the code, add proper logging
* performance improvements (LU factorization update, steepest edge)
* add benchmarks and test problems, and document how to run them (and how to run all tests)
* switch to sparse matrices (perhaps make it optional)
* make a binary that solves problems given by mps files
## Various notes
* problems in MPS format taken from https://netlib.org/lp/
* can run them with `cargo test --features benchmarks`