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

• URL: http://arxiv.org/abs/2508.15951v1
• Date: Thu, 21 Aug 2025 20:45:01 GMT
• Title: A User Manual for cuHALLaR: A GPU Accelerated Low-Rank Semidefinite Programming Solver
• Authors: Jacob Aguirre, Diego Cifuentes, Vincent Guigues, Renato D. C. Monteiro, Victor Hugo Nascimento, Arnesh Sujanani,
• Abstract summary: We present a Julia-based interface to the precompiled HALLaR and cuHALLaR binaries for large-scale semidefinite programs (SDPs)<n>Both solvers are established as fast and numerically stable, and accept problem data in formats compatible with SDPA.<n>A collection of example problems is included, including the SDP relaxations of the Matrix Completion and Maximum Stable Set problems.
• Score: 0.9236074230806578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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