ipopt-rs

A safe Rust interface to the Ipopt non-linear optimization library.

On crates.io On docs.rs

From the Ipopt webpage:

Ipopt (Interior Point OPTimizer, pronounced eye-pea-Opt) is a software package for large-scale nonlinear optimization. It is designed to find (local) solutions of mathematical optimization problems of the from

```verbatim min f(x) x in R^n

s.t. gL <= g(x) <= gU xL <= x <= xU ```

where f(x): R^n --> R is the objective function, and g(x): R^n --> R^m are the constraint functions. The vectors g_L and g_U denote the lower and upper bounds on the constraints, and the vectors x_L and x_U are the bounds on the variables x. The functions f(x) and g(x) can be nonlinear and nonconvex, but should be twice continuously differentiable. Note that equality constraints can be formulated in the above formulation by setting the corresponding components of g_L and g_U to the same value.

This crate aims to - Reduce the boilerplate, especially for setting up simple unconstrained problems - Maintain flexiblity for advanced use cases - Prevent common mistakes when defining optimization problems as early as possible using the type system and error checking.

Examples

Solve a simple unconstrained problem using L-BFGS: minimize (x - 1)^2 + (y -1)^2

```rust use approx::; use ipopt::;

struct NLP { }

impl BasicProblem for NLP { // There are two independent variables: x and y. fn numvariables(&self) -> usize { 2 }
// The variables are unbounded. Any lower bound lower than -10^9 and upper bound higher // than 10^9 is treated effectively as infinity. These absolute infinity limits can be // changed via the nlp_lower_bound_inf and nlp_upper_bound_inf Ipopt options. fn bounds(&self, xl: &mut [Number], xu: &mut [Number]) -> bool { xl.swapwithslice(vec![-1e20; 2].asmutslice()); xu.swapwithslice(vec![1e20; 2].asmut_slice()); true }

// Set the initial conditions for the solver.
fn initial_point(&self, x: &mut [Number]) {
    x.swap_with_slice(vec![0.0, 0.0].as_mut_slice());
}

// The objective to be minimized.
fn objective(&self, x: &[Number], obj: &mut Number) -> bool {
    *obj = (x[0] - 1.0)*(x[0] - 1.0) + (x[1] - 1.0)*(x[1] - 1.0);
    true
}

// Objective gradient is used to find a new search direction to find the critical point.
fn objective_grad(&self, x: &[Number], grad_f: &mut [Number]) -> bool {
    grad_f[0] = 2.0*(x[0] - 1.0);
    grad_f[1] = 2.0*(x[1] - 1.0);
    true
}

}

fn main() { let nlp = NLP { }; let mut ipopt = Ipopt::new_unconstrained(nlp).unwrap();

// Set Ipopt specific options here a list of all options is available at
// https://www.coin-or.org/Ipopt/documentation/node40.html
ipopt.set_option("tol", 1e-9); // set error tolerance
ipopt.set_option("print_level", 5); // set the print level (5 is the default)

let solve_result = ipopt.solve();

assert_eq!(solve_result.status, SolveStatus::SolveSucceeded);
assert_relative_eq!(solve_result.objective_value, 0.0, epsilon = 1e-10);
let solution = solve_result.solver_data.solution;
assert_relative_eq!(solution.primal_variables[0], 1.0, epsilon = 1e-10);
assert_relative_eq!(solution.primal_variables[1], 1.0, epsilon = 1e-10);

} ```

See the tests for more examples including constrained optimization.

Notes on the custom C API

This crate wraps Ipopt's C++ interface in a custom C API, which is significantly different than Ipopt's own C API. As such, this crate resembles the Ipopt's C++ API closer than its included C API. The motivation for maintaining a custom C API is to allow users to reuse Ipopt instances for multiple solves. For instance, the standard C API prevents users from modifying their problem structure and setting custom warm starts between solves without reconstructing solver instances. The custom C API also reduces performance costs, which can be noticeable when Ipopt is used to solve smaller subproblems.

License

This repository is licensed under the Apache License 2.0 with the exception of the following files:

which are licensed under the Eclipse Public License(EPL).