russcip

A safe Rust interface for SCIP. This crate also exposes access to the SCIP's C-API through the ffi module. The project is currently an early-stage work in progress, issues/pull-requests are very welcome.

Dependencies

Make sure SCIP is installed, the easiest way to install it is to install a precompiled package from here or through conda by running bash conda install --channel conda-forge scip After which russcip would be able to find the installation in the current Conda environment. Alternatively, you can specify the installation directory through the SCIPOPTDIR environment variable.

russcip is tested against SCIP 8.0.3 but it might work for other versions depending on which functionality you use.

Installation

By running bash cargo add russcip or to get the most recent version, add the following to your Cargo.toml toml [dependencies] russcip = { git = "https://github.com/mmghannam/russcip" }

Example

Model and solve an integer program. ```rust use russcip::model::Model; use russcip::model::ObjSense; use russcip::status::Status; use russcip::variable::VarType; use russcip::retcode::Retcode; use crate::russcip::model::ModelWithProblem;

fn main() { // Create model let mut model = Model::new() .hideoutput() .includedefaultplugins() .createprob("test") .setobjsense(ObjSense::Maximize);

// Add variables
let x1_id = model.add_var(0., f64::INFINITY, 3., "x1", VarType::Integer);
let x2_id = model.add_var(0., f64::INFINITY, 4., "x2", VarType::Integer);

// Add constraints
model.add_cons(&[x1_id, x2_id], &[2., 1.], -f64::INFINITY, 100., "c1");
model.add_cons(&[x1_id, x2_id], &[1., 2.], -f64::INFINITY, 80., "c2");

let solved_model = model.solve();

let status = solved_model.get_status();
println!("Solved with status {:?}", status);

let obj_val = solved_model.get_obj_val();
println!("Objective value: {}", obj_val);

let sol = solved_model.get_best_sol().unwrap();
let vars = solved_model.get_vars();

for var in vars {
    println!("{} = {}", &var.get_name(), sol.get_var_val(&var));
}

}

```

About SCIP

SCIP is currently one of the fastest non-commercial solvers for mixed integer programming (MIP) and mixed integer nonlinear programming (MINLP). It is also a framework for constraint integer programming and branch-cut-and-price. It allows for total control of the solution process and the access of detailed information down to the guts of the solver.