Julia interfaces

This page shows how to use the Julia interface of HPR-QP: run demos (MPS / MAT), build custom convex quadratic programs (CQP), and solve QAP/LASSO instances.

Usage 1: Test Instances in MPS (and MAT for QAP/LASSO)

Setting Data and Result Paths

Before running the scripts, modify demo/run_single_file.jl or demo/run_dataset.jl to set your data path and result path.

Running a Single Instance

To test a single instance:

julia --project demo/run_single_file.jl

Running All Instances in a Directory

To process all files in a directory:

julia --project demo/run_dataset.jl

Notes

  • QAP instances (MAT): The .mat file should include the matrices A, B, S, T.
    See HPR-QP_QAP_LASSO/demo/demo_QAP.jl for how to generate such files. (Also refer to Section 4.5 of the paper.)

  • LASSO instances (MAT): The .mat file should contain matrix A and vector b.

Usage 2: Define your CQP Model in Julia Scripts

Example 1: Build & export a CQP with JuMP, then solve via HPR-QP

This example demonstrates how to construct a CQP model using the JuMP modeling language in Julia and export it to MPS format for use with the HPR-QP solver.

julia --project demo/demo_JuMP.jl

The script:

  • Builds a CQP model.

  • Saves the model as an MPS file

  • Uses HPR-QP to solve the CQP instance

Remark: If a model may be infeasible/unbounded, you can check it with HiGHS.

using JuMP, HiGHS

# Read a model from file (or create it elsewhere)
mps_file_path = "xxx"  # your file path
model = read_from_file(mps_file_path)

# Set HiGHS as the optimizer
set_optimizer(model, HiGHS.Optimizer)

# Solve it
optimize!(model)

Example 2: Define a small CQP directly in Julia (no JuMP)

This example demonstrates how to construct and solve a CQP problem directly in Julia without relying on JuMP.

julia --project demo/demo_QAbc.jl

Example 3: Generate a random LASSO instance in Julia

This example demonstrates how to construct and solve a random LASSO instance.

julia --project demo/demo_LASSO.jl

Note on First-Time Execution Performance

You may notice that solving a single instance — or the first instance in a dataset — appears slow. This is due to Julia’s Just-In-Time (JIT) compilation, which compiles code on first execution.

Tip

Tip for Better Performance:
To reduce repeated compilation overhead, it’s recommended to run scripts from an IDE like VS Code or the Julia REPL in the terminal.

Start Julia REPL with the project environment:

julia --project

Then, at the Julia REPL, run demo/demo_QAbc.jl (or other scripts):

include("demo/demo_QAbc.jl")

CAUTION

If you encounter the error message:

Error: Error during loading of extension AtomixCUDAExt of Atomix, use Base.retry_load_extensions() to retry.


This is usually transient. Wait a few moments; the extension typically loads successfully on its own.