A User Manual for cuHALLaR: A GPU Accelerated Low-Rank Semidefinite Programming Solver
This provides a user-friendly tool for researchers and practitioners working on semidefinite programming, but it is incremental as it focuses on interface development rather than new algorithmic breakthroughs.
The authors introduced a Julia interface for HALLaR and cuHALLaR solvers to handle large-scale semidefinite programs, enabling users to load custom data, configure options, and run experiments with included examples like Matrix Completion and Maximum Stable Set problems.
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.