A User Manual for cuHALLaR: A GPU Accelerated Low-Rank Semidefinite Programming Solver

Jacob Aguirre, Diego Cifuentes, Vincent Guigues, Renato D. C. Monteiro, Victor Hugo Nascimento +1 more
8/21/2025
math.OCcs.LGcs.MS

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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.

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Cite this paper

@article{aguirre2025user,
  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},
  year   = {2025},
  eprint = {2508.15951},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2508.15951v1}
}

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