A User Manual for cuHALLaR: A GPU Accelerated Low-Rank Semidefinite Programming Solver
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Abstract (arXiv)
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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References · 5
- 2018Complexity of a quadratic penalty accelerated inexact proximal point method for solving linearly constrained nonconvex composite programscited by 15
- 2024A low-rank augmented Lagrangian method for large-scale semidefinite programming based on a hybrid convex-nonconvex approachcited by 4
- 2022An adaptive superfast inexact proximal augmented Lagrangian method for smooth nonconvex composite optimization problemscited by 3
- 2018An efficient adaptive accelerated inexact proximal point method for solving linearly constrained nonconvex composite problemscited by 2
- 2025cuHALLaR: A GPU Accelerated Low-Rank Augmented Lagrangian Method for Large-Scale Semidefinite Programmingcited by 1
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