This project provides a mathematical programming modeling library for Rust.
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 that require a separate installation (see below the examples section) to be present at runtime of your lp_modeler-based project are:
Presently supported solvers that you can import as Rust crates (as optional features) are:
- minilp
- coin_cbc (requires the
Cbclibrary files to be present at compile time of yourlp_modeler-based project, see thecoin_cbcproject README for how to do this)
This project is inspired by COIN-OR PuLP which provides such a library for Python.
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.
\ 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
extern crate lp_modeler;
use lp_modeler::solvers::{CbcSolver, SolverTrait};
use lp_modeler::dsl::*;
use lp_modeler::constraint;
fn main() {
// 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 += constraint!(500*a + 1200*b + 1500*c <= 10000);
// Constraint: a <= b
problem += constraint!(a <= b);
// Specify solver
let solver = CbcSolver::new();
// Run optimisation and process output hashmap
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),
}
}To generate the LP file which is shown above:
problem.write_lp("problem.lp")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.
| Abe | Ben | Cam | |
|---|---|---|---|
| Deb | 50 | 60 | 60 |
| Eve | 75 | 95 | 70 |
| Fay | 75 | 80 | 80 |
This problem is formulated as an Assignment Problem.
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 (status, results) = 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 = results.get(&var.name).unwrap();
obj_value += obj_coef * var_value;
}
// Print output
println!("Status: {:?}", status);
println!("Objective Value: {}", obj_value);
for (var_name, var_value) in &results{
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
If you want the latest release version of Cbc, Gurobi or GLPK, the easiest cross-platform installation pathway should be via conda. Importantly, this does not require admin rights on the system you want to install it on. All you need to do is install conda. Once this is done, use the respective conda command for the solver you want to use (see below).
To get the latest Cbc release for your system with conda (installation see above), use this command:
conda create -n coin-or-cbc -c conda-forge coin-or-cbc
Then activating the newly created environment will make the cbc executable available:
conda activate coin-or-cbc
To get the latest Cbc release, including the .
We recommend using COIN-OR's coinbrew, as described here:
https://coin-or.github.io/user_introduction#building-from-source
To get the very latest Cbc version, including unreleased bug fixes, you will need to build it from source.
We recommend using COIN-OR's coinbrew, as described here:
https://coin-or.github.io/user_introduction#building-from-source
To get a recent release of GLPK for your system with conda, use this command:
conda create -n glpk -c conda-forge glpk
Then activating the newly created environment will make the glpsol executable available:
conda activate glpk
To use Gurobi, you need to have a valid license key and have it in a location that Gurobi can find it. Once you have a valid license, you can get the latest Gurobi release for your system with conda, use this command:
conda create -n gurobi -c gurobi gurobi
Then activating the newly created environment will make the gurobi_cl executable available:
conda activate gurobi
- Add a native
minilpimpl to call the Rust native solverminilp - Changed
coin_cbc-basedNativeCbcSolverto an optional feature - Fix adding upper bounds to
NativeCbc - Add a
coinstraint!()macro - Add
AddAssign,SubAssignandMulAssigntraits - Reworked various internal functions to remove recursions (fixes related stack overflows)
- Add install infos for the solvers to the docs
- Add a native coin-or impl (NativeCBCSolver) to call CoinOR CBC trough the C API.
- Fix incorrect simplification of (expr-c1)+c2
- Fix failed cbc parsing on infeasible solution
-
Improve modules
- Remove maplit dependency
- All the features to write expressions and constraints are put into
dslmodule use lp_modeler::dsl::*is enough to write a systemuse lp_modeler::solvers::*is always used to choose a solver
-
Add a
sum()method for vector ofLpExpression/Into<LpExpression>instead oflp_sum()function -
Add a
sum()function used in the form:problem += sum(&vars, |&v| v * 10.0) ).le(10.0);
- Fix and improve error message for GLPK solution parsing
- Format code with rust fmt
- Add new examples in documentation
- Improve 0.0 comparison
- Add distributive property (ex:
3 * (a + b + 2) = 3*a + 3*b + 6) - Add trivial rules (ex:
3 * a * 0 = 0or3 + 0 = 3) - Add commutative property to simplify some computations
- Support for GLPK
- Functional lib with simple algebra properties
- Joel Cavat (jcavat)
- Thomas Vincent (tvincent2)
- Antony Phillips (aphi)
- Florian B. (Lesstat)
- Amila Welihinda (amilajack)
- (zappolowski)
- Yisu Rem Wang (remysucre)
- Tony Cox (tony-cox)
- EdorianDark
- Colman Humphrey (ColmanHumphrey)
- Stephan Beyer sbeyer
- Ophir Lojkine lovasoa
- David Lähnemann dlaehnemann
- 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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