MATLAB Binding of HPR-LP-C

This page provides quick-start examples for using the MATLAB interfaces of HPR-LP-C to build and solve linear programs directly from matrices or MPS files.

Test problem (LP)

The default demo problem we ship is a small linear program of the form

\[\begin{split} \begin{aligned} \min \quad & - 3 x_1 - 5 x_2\\ \text{s.t.} \quad & x_1 + 2 x_2 \le 10, \\ & 3 x_1 + x_2 \le 12, \\ & x_1 \ge 0, \\ & x_2 \ge 0. \end{aligned} \end{split}\]

Example 1: Build model directly from matrices

cd examples
matlab -batch "example_direct_lp"              # Solve from arrays

Or run interactively in MATLAB:

cd examples
example_direct_lp

Quick overview: The following snippet demonstrates how to define and solve an LP problem directly from matrices. For a complete version with additional options, see example_direct_lp.m.

% Define LP: minimize c'x subject to AL <= Ax <= AU, l <= x <= u
A = sparse([1.0, 2.0; 3.0, 1.0]);
AL = [-inf; -inf];
AU = [10.0; 12.0];
l = [0.0; 0.0];
u = [inf; inf];
c = [-3.0; -5.0];

% Create model
model = hprlp.Model.from_arrays(A, AL, AU, l, u, c);

% Configure solver
param = hprlp.Parameters();
param.stop_tol = 1e-9;
param.device_number = 0;

% Solve
result = model.solve(param);

if strcmp(result.status, 'OPTIMAL')
    fprintf('Optimal: %.6f\n', result.primal_obj);
    fprintf('Solution: x = [%.6f, %.6f]\n', result.x(1), result.x(2));
end

hprlp.Model.from_arrays(A, AL, AU, l, u, c)

Create an LP model from matrices and vectors.

Arguments:

  • A - Constraint matrix (m×n sparse or dense)

  • AL, AU - Constraint bounds (column vectors)

  • l, u - Variable bounds (column vectors)

  • c - Objective coefficients (column vector)

Returns: Model object

Example 2: Solve from MPS File

cd examples
matlab -batch "example_mps_file"              # Solve from MPS file

Or run interactively in MATLAB:

cd examples
example_mps_file

Quick overview: The following snippet demonstrates how to define and solve an LP problem from an MPS file. For a complete version with additional options, see example_mps_file.m.

% Create model from MPS file
model = hprlp.Model.from_mps('problem.mps');

% Solve
result = model.solve();
disp(result);

hprlp.Model.from_mps(filename)

Create an LP model from the MPS file.

Arguments:

  • filename - Path to MPS file

Returns: Model object

model.solve(param)

Solve the LP model.

Arguments:

  • param - (Optional) Parameters object. If omitted, default parameters are used.

Returns: Result object

hprlp.Parameters

Solver configuration:

  • max_iter - Maximum iterations (default: 2147483647)

  • stop_tol - Stopping tolerance (default: 1e-4)

  • time_limit - Time limit in seconds (default: 3600)

  • device_number - CUDA device ID (default: 0)

  • check_iter - Convergence check interval (default: 150)

  • use_Ruiz_scaling - Ruiz scaling (default: true)

  • use_Pock_Chambolle_scaling - Pock-Chambolle scaling (default: true)

  • use_bc_scaling - Bounds/cost scaling (default: true)

Example:

param = hprlp.Parameters();
param.stop_tol = 1e-9;
param.device_number = 0;

See the github for more details.