快速求解大规模半定规划问题的GPU加速工具包
A User Manual for cuHALLaR: A GPU Accelerated Low-Rank Semidefinite Programming Solver
- 基于Julia封装HALLaR和cuHALLaR，支持混合稀疏低秩结构
- 兼容SDPA格式与新增强格式，提升大规模问题求解效率
- 适合需要高性能半定规划求解的研究者与工程师
我们提出了一个基于Julia的接口，用于调用预编译的HALLaR和cuHALLaR二进制文件，以求解大规模半定规划（SDP）问题。这两个求解器已被证明具有高速度和数值稳定性，支持与SDPA兼容的输入格式以及一种利用混合稀疏低秩（HSLR）结构的新增强格式。该接口允许用户直接从Julia加载自定义数据文件、配置求解选项并执行实验。附带一组示例问题，包括矩阵补全和最大稳定集问题的SDP松弛实例。
原文摘要 · Abstract (English)
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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