Expand description
A pure-Rust Interior Point solver for linear programs with equality and inequality constraints.
Linear programs
A linear program is a mathematical optimization problem defined as:
min_x c'x
st A_eq'x == b_eq
A_ub'x <= b_ub
x >= 0
Example
use approx::assert_abs_diff_eq;
use ndarray::array;
use ripped::prelude::*;
let A_ub = array![[-3f64, 1.], [1., 2.]];
let b_ub = array![6., 4.];
let A_eq = array![[1., 1.]];
let b_eq = array![1.];
let c = array![-1., 4.];
let problem = Problem::target(&c)
// If you define neither equality nor inequality constraints,
// the problem returns as unconstrained.
.ub(&A_ub, &b_ub)
.eq(&A_eq, &b_eq)
.build()
.unwrap();
// These are the default values you can overwrite.
// You may omit any option for which the default is good enough for you
let solver = InteriorPoint::custom()
.solver_type(EquationSolverType::Cholesky)
.tol(1e-8)
.disp(false)
.ip(true)
.alpha0(0.99995)
.max_iter(1000)
.build()
.unwrap();
let res = solver.solve(&problem).unwrap();
assert_abs_diff_eq!(res.x(), &array![1., 0.], epsilon = 1e-6);Feature flags
[linfa-linalg] backend for pure-rust linear algebra:
This feature is enabled by default. Disable this if you opt for one of the LAPACK options below.
For details on this backend, see the documentation of linfa-linalg.
LAPACK backend for linear algebra (and BLAS for matrix operations):
[netlib-system][netlib-system][openblas-static][openblas-system][intel-mkl-static][intel-mkl-system]
If you don’t mind compiling and/or linking one of the reference LAPACK implementations, then one of these options
is for you. These features are forwarded to ndarray-linalg,
see their documentation for details on how to compile/link each backend.
Modules
- Error type used by solvers and pre-solvers.
- Definition of a linear program.
- Commonly used imports; use as
use ripped::prelude::* - Solvers for linear programs.