lp-modeler

A linear programming modeller written in Rust. This api helps to write LP model and use solver such as CBC, Gurobi, lp_solve, …

7 releases

 new 0.4.2 Oct 14, 2019 Oct 8, 2019 Sep 26, 2019 Sep 23, 2019 Aug 7, 2017

#36 in Algorithms

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lp-modeler

This project provides a mathematical programming modeling library for the Rust language (v1.28+).

An optimization problem (e.g. an integer or linear programme) can be formulated using familiar Rust syntax (see examples), and written into a universal LP model format. This can then be processed by a mixed integer programming solver. Presently supported solvers are; COIN-OR CBC, Gurobi and GLPK.

This project is inspired by COIN-OR PuLP which provides such a library for Python.

Usage

These examples present a formulation (in LP model format), and demonstrate the Rust code required to generate this formulation. Code can be found in tests/problems.rs.

Example 1 - Simple model

Formulation

\ One Problem

Maximize
10 a + 20 b

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

Generals
a c b

End

Rust code

use lp_modeler::problem::{LpObjective, Problem, LpProblem};
use lp_modeler::operations::{LpOperations};
use lp_modeler::variables::LpInteger;
use lp_modeler::solvers::{SolverTrait, CbcSolver};

// Define problem variables
let ref a = LpInteger::new("a");
let ref b = LpInteger::new("b");
let ref c = LpInteger::new("c");

// Define problem and objective sense
let mut problem = LpProblem::new("One Problem", LpObjective::Maximize);

// Objective Function: Maximize 10*a + 20*b
problem += 10.0 * a + 20.0 * b;

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

// Constraint: a <= b
problem += (a).le(b);

// Specify solver
let solver = CbcSolver::new();

// Run optimisation and process output hashmap
match solver.run(&problem) {
Ok((status, var_values)) => {
println!("Status {:?}", status);
for (name, value) in var_values.iter() {
println!("value of {} = {}", name, value);
}
},
Err(msg) => println!("{}", msg),
}

To generate the LP file which is shown above:

problem.write_lp("problem.lp")

Example 2 - An Assignment model

Formulation

This more complex formulation programmatically generates the expressions for the objective and constraints.

We wish to maximise the quality of the pairing between a group of men and women, based on their mutual compatibility score. Each man must be assigned to exactly one woman, and vice versa.

Compatibility Score Matrix
Abe Ben Cam
Deb 50 60 60
Eve 75 95 70
Fay 75 80 80

This problem is formulated as an Assignment Problem.

Rust code

extern crate lp_modeler;

use std::collections::HashMap;

use lp_modeler::dsl::*;
use lp_modeler::solvers::{SolverTrait, CbcSolver};

fn main() {
// Problem Data
let men = vec!["A", "B", "C"];
let women = vec!["D", "E", "F"];
let compatibility_score: 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 vars: HashMap<(&str,&str), LpBinary> =
men.iter()
.flat_map(|&m| women.iter()
.map(move |&w| {
let key = (m,w);
let value = LpBinary::new(&format!("{}_{}", m,w));
(key, value)
}))
.collect();

// Define Objective Function
let obj_vec: Vec<LpExpression> = {
vars.iter().map( |(&(m,w), bin)| {
let &coef = compatibility_score.get(&(m, w)).unwrap();
coef * bin
} )
}.collect();
problem += obj_vec.sum();

// Define Constraints
// - constraint 1: Each man must be assigned to exactly one woman
for &m in &men{
problem += sum(&women, |&w| vars.get(&(m,w)).unwrap() ).equal(1);
}

// - constraint 2: Each woman must be assigned to exactly one man
for &w in &women{
problem += sum(&men, |&m| vars.get(&(m,w)).unwrap() ).equal(1);
}

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

// Compute final objective function value
// (terminate if error, or assign status & variable values)
assert!(result.is_ok(), result.unwrap_err());
let (solver_status, var_values) = result.unwrap();
let mut obj_value = 0f32;
for (&(m, w), var) in &vars{
let obj_coef = compatibility_score.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);
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);
}
}

}

This code computes the objective function value and processes the output to print the chosen pairing of men and women:

Status: Optimal
Objective Value: 230
B_E = 1
C_D = 1
A_F = 1

Changelog

0.4.2

• Fix incorrect simplification of (expr-c1)+c2

0.4.1

• Fix failed cbc parsing on infeasible solution

0.4.0

• Improve modules

• Remove maplit dependency
• All the features to write expressions and constraints are put into dsl module
• use lp_modeler::dsl::* is enough to write a system
• use lp_modeler::solvers::* is always used to choose a solver
• Add a sum() method for vector of LpExpression/Into<LpExpression> instead of lp_sum() function

• Add a sum() function used in the form:

problem += sum(&vars, |&v| v * 10.0) ).le(10.0);

0.3.3

• Fix and improve error message for GLPK solution parsing
• Format code with rust fmt

0.3.3

• Add new examples in documentation
• Improve 0.0 comparison

0.3.1

• Add distributive property (ex: 3 * (a + b + 2) = 3*a + 3*b + 6)
• Add trivial rules (ex: 3 * a * 0 = 0 or 3 + 0 = 3)
• Add commutative property to simplify some computations
• Support for GLPK

0.3.0

• Functional lib with simple algebra properties

Further work

• Parse and provide the objective value
• Config for lp_solve and CPLEX
• It would be great to use some constraint for binary variables such as
• a && b which is the constraint a + b = 2
• a || b which is the constraint a + b >= 1
• a <=> b which is the constraint a = b
• a => b which is the constraint a <= b
• All these cases are easy with two constraints but more complex with expressions
• ...

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