ipopt-rs

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

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

   min     f(x)
   x in R^n

   s.t.       g_L <= g(x) <= g_U
              x_L <=  x   <= x_U

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

use approx::*;
use ipopt::*;

struct NLP {
}

impl BasicProblem for NLP {
    // There are two independent variables: x and y.
    fn num_variables(&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, x_l: &mut [Number], x_u: &mut [Number]) -> bool {
        x_l.swap_with_slice(vec![-1e20; 2].as_mut_slice());
        x_u.swap_with_slice(vec![1e20; 2].as_mut_slice());
        true
    }

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

    // 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.

Getting Ipopt Binaries

As it stands, this library is still immature in terms of platform support. There is ongoing work to improve this. Currently the supported methods for acquiring the Ipopt libraries is by building it against MKL on macOS and downloading binaries from JuliaOpt on Linux. Windows is not currently supported until I get a Windows machine or somebody else pitches in to provide the support ;) The rough plan for getting Ipopt binaries is currently outlined in the build.rs script, but it may change in the future.

Notes on the custom C API

This crate wraps Ipopt's C++ interface in a custom C API called CNLP, which is significantly different than Ipopt's own C API. In fact, 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 CNLP also reduces performance costs, which can be noticeable when Ipopt is used to solve smaller sub-problems.

License

This repository is licensed under the Apache License 2.0.