problems.rs

extern crate lp_modeler;

use std::collections::HashMap;

use lp_modeler::solvers::{CbcSolver, SolverTrait, Solution};
#[cfg(feature = "native_coin_cbc")]
use lp_modeler::solvers::NativeCbcSolver;
use lp_modeler::dsl::*;
use lp_modeler::format::lp_format::LpFileFormat;

#[test]
fn test_readme_example_1() {
    let ref a = LpInteger::new("a");
    let ref b = LpInteger::new("b");
    let ref c = LpInteger::new("c");

    let mut problem = LpProblem::new("One Problem", LpObjective::Maximize);
    problem += 10.0 * a + 20.0 * b;

    problem += (500 * a + 1200 * b + 1500 * c).le(10000);
    problem += (a).le(b);

    let solver = CbcSolver::new();

    match solver.run(&problem) {
        Ok( solution ) => {
            println!("Status {:?}", solution.status);
            for (name, value) in solution.results.iter() {
                println!("value of {} = {}", name, value);
            }
        }
        Err(msg) => println!("{}", msg),
    }

    let output1 = "\\ One Problem

Maximize
  obj: 10 a + 20 b

Subject To
  c1: 500 a + 1200 b + 1500 c <= 10000
  c2: a - b <= 0

"
        .to_string();
    let output2 = problem.to_lp_file_format();
    let output2 = output2.split("Generals").collect::<Vec<&str>>();
    let output2 = output2[0];
    assert_eq!(output1, output2);
}

#[test]
fn test_full_example() {
    let ref a = LpInteger::new("a").lower_bound(1.0);
    let ref b = LpInteger::new("b").upper_bound(10.0);
    let ref c = LpInteger::new("c").lower_bound(2.0).upper_bound(8.5);
    let ref d = LpBinary::new("d");
    let ref e = LpContinuous::new("e");

    let mut problem = LpProblem::new("One Problem", LpObjective::Maximize);
    problem += a + b + c + d + e;

    problem += (a + b + c + d + e).le(100.0);

    let solver = CbcSolver::new();

    match solver.run(&problem) {
        Ok( solution ) => {
            println!("Status {:?}", solution.status);
            for (name, value) in solution.results.iter() {
                println!("value of {} = {}", name, value);
            }
        }
        Err(msg) => println!("{}", msg),
    }

    let output1 = problem.to_lp_file_format();
    for expr in vec!("e free", "1 <= a", "2 <= c <= 8.5", "b <= 10") {
        assert!(output1.contains(expr), format!("{} is not present",expr));
    }
}

#[test]
fn test_readme_example_2() {
    // Problem Data
    let men = vec!["A", "B", "C"];
    let women = vec!["D", "E", "F"];
    let compat_scores : HashMap<(&str,&str),f32> = vec![
        (("A", "D"), 50.0),
        (("A", "E"), 75.0),
        (("A", "F"), 75.0),
        (("B", "D"), 60.0),
        (("B", "E"), 95.0),
        (("B", "F"), 80.0),
        (("C", "D"), 60.0),
        (("C", "E"), 70.0),
        (("C", "F"), 80.0),
    ].into_iter().collect();

    // Define Problem
    let mut problem = LpProblem::new("Matchmaking", LpObjective::Maximize);

    // Define Variables
    let mut vars = HashMap::new();
    for m in &men {
        for w in &women {
            vars.insert((m, w), LpBinary::new(&format!("{}_{}", m, w)));
        }
    }

    // Define Objective Function
    let mut obj_vec: Vec<LpExpression> = Vec::new();
    for (&(&m, &w), var) in &vars {
        let obj_coef = compat_scores.get(&(m, w)).unwrap();
        obj_vec.push(*obj_coef * var);
    }
    problem += lp_sum(&obj_vec);

    // Define Constraints
    // Constraint 1: Each man must be assigned to exactly one woman
    for m in &men {
        let mut constr_vec = Vec::new();

        for w in &women {
            constr_vec.push(1.0 * vars.get(&(m, w)).unwrap());
        }

        problem += lp_sum(&constr_vec).equal(1);
    }

    // Constraint 2: Each woman must be assigned to exactly one man
    for w in &women {
        let mut constr_vec = Vec::new();

        for m in &men {
            constr_vec.push(1.0 * vars.get(&(m, w)).unwrap());
        }

        problem += lp_sum(&constr_vec).equal(1);
    }

    // Optionally write to file
    // let result = problem.write_lp("problem.lp");
    // match result{
    //     Ok(_) => println!("Written to file"),
    //     Err(msg) => println!("{}", msg)
    // }

    // Run Solver
    let solver = CbcSolver::new();
    let result = solver.run(&problem);

    // Terminate if error, or assign status & variable values
    assert!(result.is_ok(), result.unwrap_err());
    let Solution { status: solver_status, results: var_values, related_problem: _ } = result.unwrap();

    // Compute final objective function value
    let mut obj_value = 0f32;
    for (&(&m, &w), var) in &vars {
        let obj_coef = compat_scores.get(&(m, w)).unwrap();
        let var_value = var_values.get(&var.name).unwrap();

        obj_value += obj_coef * var_value;
    }

    // Print output
    println!("Status: {:?}", solver_status);
    println!("Objective Value: {}", obj_value);
    // println!("{:?}", var_values);
    for (var_name, var_value) in &var_values {
        let int_var_value = *var_value as u32;
        if int_var_value == 1 {
            println!("{} = {}", var_name, int_var_value);
        }
    }

    assert_eq!(solver_status, lp_modeler::solvers::Status::Optimal);
    assert_eq!(obj_value, 230f32);
    assert_eq!(*var_values.get("A_F").unwrap(), 1f32);
    assert_eq!(*var_values.get("B_E").unwrap(), 1f32);
    assert_eq!(*var_values.get("C_D").unwrap(), 1f32);
}

#[cfg(feature = "native_coin_cbc")]
#[test]
// as in https://github.com/KardinalAI/coin_cbc/blob/master/examples/knapsack.rs
//
// Maximize  5a + 3b + 2c + 7d - 4e
// s.t.      2a - 8b + 4c + 2d + 5e <= 10
fn cbc_native_optimal() {
    let mut problem = LpProblem::new("Knapsack", LpObjective::Maximize);
    let objective: HashMap<&str, f32> =
        vec![("a", 5.0), ("b", 3.0), ("c", 2.0), ("d", 7.0), ("e", 4.0)]
            .into_iter()
            .collect();
    let x: HashMap<&str, LpBinary> = objective
        .iter()
        .map(|(name, _)| (*name, LpBinary::new(name)))
        .collect();
        problem +=
        (2.0 * &x["a"] - 8.0 * &x["b"] + 4.0 * &x["c"] + 2. * &x["d"] + 5. * &x["e"]).le(10.);
    problem += 5.0 * &x["a"] + 3.0 * &x["b"] + 2.0 * &x["c"] + 7. * &x["d"] + -4. * &x["e"];

    let solver = NativeCbcSolver::new();

    match solver.run(&problem) {
        Ok(sol) => {
            println!("Status {:?}", sol.status);
            println!("{:?}", sol.results);
            assert_eq!(
                17f32,
                x.iter()
                    .map(|(name, var)| match sol.results.get(&var.name) {
                        Some(s) => {
                            println!("{:?}*{}", s, objective.get(name).unwrap());
                            s * objective.get(name).unwrap()
                        }
                        _ => 0.,
                    })
                    .sum()
            );
        }
        Err(msg) => panic!("Native Cbc Solver panicked at run: {}", msg),
    }
}