The linear equations that arise in interior methods for constrained optimization are sparse symmetric indefinite and become extremely ill-conditioned as the interior method converges. These linear systems present a challenge for existing solver frameworks based on sparse LU or LDL^T decompositions. We benchmark five well known direct linear solver packages using matrices extracted from power grid optimization problems. The achieved solution accuracy varies greatly among the packages. None of the tested packages delivers significant GPU acceleration for our test cases.

中文翻译：

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用于电网优化问题的线性求解器：GPU 加速线性求解器回顾

用于约束优化的内部方法中出现的线性方程是稀疏对称不定的，并且随着内部方法的收敛变得极其病态。这些线性系统对基于稀疏 LU 或 LDL^T 分解的现有求解器框架提出了挑战。我们使用从电网优化问题中提取的矩阵对五个众所周知的直接线性求解器包进行基准测试。所获得的解决方案精度因软件包而异。没有一个测试包为我们的测试用例提供显着的 GPU 加速。