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

TL;DR

This paper introduces a Julia interface for cuHALLaR, a GPU-accelerated solver for large-scale semidefinite programs, enabling easier experimentation and problem data management.

Contribution

It provides a user-friendly Julia interface to cuHALLaR, supporting custom data, configuration, and example problems, enhancing accessibility for large-scale SDP solving.

Findings

cuHALLaR is fast and numerically stable

Supports multiple data formats including HSLR

Includes example problems like matrix completion

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.