A Linear Programming modeler that is easy to use, performant with large problems, and well-typed.
```rust use goodlp::{variables, variable, coincbc, 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)); } ```
You can find a resource allocation problem example in
resource_allocation_problem.rs.
This library offers an abstraction over multiple solvers. By default, it uses cbc, but you can also activate other solvers using cargo features.
Used by default, performant, but requires to have a C compiler and the cbc C library installed.
minilp is a pure rust solver, which means it works out of the box without installing anything else.
You can activate it with :
toml
[dependencies.good_lp]
version = "0.1.0"
default-features = false
features = ["minilp"]
Then use minilp instead of coin_cbc in your code:
```rust
use good_lp::minilp;
fn optimize