Python Version

This page shows how to use the Python interface of HPR-LP: run demos, build custom problems, adjust solver settings, and interpret results.

---

Running examples

The demo LP problem is:

Test problem (LP)

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

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

---

After installing the environment, you can run the solver in two ways: model-based API or .mps API.

1. Model-based API

You can also build and solve a small demo script under demo/demo_model_api.py.

python demo/demo_model_api.py

2. MPS file API

python scripts/run_single_file.py

This calls run_single_file.py with its default arguments, which solve one LP model in MPS format.

You can also override any of these from the command line, for example:

python scripts/run_single_file.py \
    --file_name /path/to/your_problem.mps \
    --device_number 0 \
    --stoptol 1e-8 \
    --time_limit 3600 \
    --check_iter 150 \
    --warm_up True \
    --use_Ruiz_scaling True \
    --use_Pock_Chambolle_scaling True \
    --use_bc_scaling True

Parameters

Below is a list of the parameters in HPR-LP along with their default values and usage:

Parameter	Default Value	Description
warm_up	false	Determines if a warm-up phase is performed before main execution.
time_limit	3600	Maximum allowed runtime (seconds) for the algorithm.
stoptol	1e-4	Stopping tolerance for convergence checks.
device_number	0	GPU device number (only relevant if use_gpu is true).
max_iter	typemax(Int32)	Maximum number of iterations allowed.
check_iter	150	Number of iterations to check residuals.
use_Ruiz_scaling	true	Whether to apply Ruiz scaling.
use_Pock_Chambolle_scaling	true	Whether to use the Pock-Chambolle scaling.
use_bc_scaling	true	Whether to use the scaling for b and c.
use_gpu	true	Whether to use GPU or not.
print_frequency	-1 (auto)	Print the log every print_frequency iterations.

Results

After solving an instance, you can access the result variables as shown below:

Category	Variable	Description
Iteration Counts	iter	Total number of iterations performed by the algorithm.
	iter_4	Number of iterations required to achieve an accuracy of 1e-4.
	iter_6	Number of iterations required to achieve an accuracy of 1e-6.
Time Metrics	time	Total time in seconds taken by the algorithm.
	time_4	Time in seconds taken to achieve an accuracy of 1e-4.
	time_6	Time in seconds taken to achieve an accuracy of 1e-6.
	power_time	Time in seconds used by the power method.
Objective Values	primal_obj	The primal objective value obtained.
	gap	The gap between the primal and dual objective values.
Residuals	residuals	Relative residuals of the primal feasibility, dual feasibility, and duality gap.
Algorithm Status	output_type	The final status of the algorithm: OPTIMAL : Found optimal solution MAX_ITER : Max iterations reached TIME_LIMIT : Time limit reached
Solution Vectors	x	The final solution vector x.
	y	The final solution vector y.
	z	The final solution vector z.