A Linear Programming modeler that is easy to use, performant with large problems, and well-typed.

good_lp

A Linear Programming modeler that is easy to use, performant with large problems, and well-typed.

use good_lp::{variables, variable, coin_cbc, SolverModel, Solution};

fn main() {
    let mut vars = variables!();
    let a = vars.add(variable().max(1));
    let b = vars.add(variable().min(2).max(4));
    let solution = vars.maximise(10 * (a - b / 5) - b)
        .using(coin_cbc)
        .with(a + 2 << b) // or (a + 2).leq(b)
        .with(1 + a >> 4 - b) // or (1 + a).geq(4 - b)
        .solve()?;
    println!("a={}   b={}", solution.value(a), solution.value(b));
    println!("a + b = {}", solution.eval(a + b));
}

Features and limitations

• Linear programming. This crate currently supports only the definition of linear programs. You cannot use it with quadratic functions, for instance.
• Continuous variables. Currently, only continuous variables are supported. Mixed-integer linear programming (MILP) is not supported.
• Not a solver. This crate uses other rust crates to provide the solvers. There is no solving algorithm in good_lp itself. If you have an issue with a solver, report it to the solver directly. See below for the list of supported solvers.

Contributing

Pull requests are welcome ! If you need any of the features mentioned above, get in touch. Also, do not hesitate to open issues to discuss the implementation.

Alternatives

If you need non-linear programming or integer variables, you can use lp-modeler. However, it is currently very slow with large problems.

You can also directly use the underlying solver libraries, such as coin_cbc or minilp if you don't need a way to express your objective function and constraints using an idiomatic rust syntax.

Usage examples

You can find a resource allocation problem example in resource_allocation_problem.rs.

Solvers

This library offers an abstraction over multiple solvers. By default, it uses cbc, but you can also activate other solvers using cargo features.

cbc

Used by default, performant, but requires to have a C compiler and the cbc C library installed.

In ubuntu, you can install it with:

sudo apt-get install coinor-cbc coinor-libcbc-dev

In MacOS, using homebrew :

brew install cbc

minilp

minilp is a pure rust solver, which means it works out of the box without installing anything else.

You can activate it with :

[dependencies.good_lp]
version = "0.1.0"
default-features = false
features = ["minilp"]

Then use minilp instead of coin_cbc in your code:

use good_lp::minilp;

fn optimize<V>(vars: ProblemVariables<V>) {
    vars.maximise(objective).using(minilp);
}

License

This library is published under the MIT license.