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)); } ```
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.
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.
In ubuntu, you can install it with:
sudo apt-get install coinor-cbc coinor-libcbc-dev
In MacOS, using homebrew :
brew install cbc
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
This library is published under the MIT license.