A User Manual for cuHALLaR: A GPU Accelerated Low-Rank Semidefinite Programming Solver
Optimization and Control
2025-08-25 v1 Machine Learning
Mathematical Software
Numerical Analysis
Numerical Analysis
Abstract
We present a Julia-based interface to the precompiled HALLaR and cuHALLaR binaries for large-scale semidefinite programs (SDPs). Both solvers are established as fast and numerically stable, and accept problem data in formats compatible with SDPA and a new enhanced data format taking advantage of Hybrid Sparse Low-Rank (HSLR) structure. The interface allows users to load custom data files, configure solver options, and execute experiments directly from Julia. A collection of example problems is included, including the SDP relaxations of the Matrix Completion and Maximum Stable Set problems.
Cite
@article{arxiv.2508.15951,
title = {A User Manual for cuHALLaR: A GPU Accelerated Low-Rank Semidefinite Programming Solver},
author = {Jacob Aguirre and Diego Cifuentes and Vincent Guigues and Renato D. C. Monteiro and Victor Hugo Nascimento and Arnesh Sujanani},
journal= {arXiv preprint arXiv:2508.15951},
year = {2025}
}