{"ID":2880007,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.15951","arxiv_id":"2508.15951","title":"A User Manual for cuHALLaR: A GPU Accelerated Low-Rank Semidefinite Programming Solver","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.","short_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...","url_abs":"https://arxiv.org/abs/2508.15951","url_pdf":"https://arxiv.org/pdf/2508.15951v1","authors":"[\"Jacob Aguirre\",\"Diego Cifuentes\",\"Vincent Guigues\",\"Renato D. C. Monteiro\",\"Victor Hugo Nascimento\",\"Arnesh Sujanani\"]","published":"2025-08-21T20:45:01Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"cs.LG\",\"cs.MS\",\"math.NA\"]","methods":"[]","has_code":false}