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HDSDP: Software for Semidefinite Programming

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arxiv 2207.13862 v2 pith:A7576VIT submitted 2022-07-28 cs.MS math.OC

Wenzhi Gao , Dongdong Ge , Yinyu Ye This is my paper · ORCID
classification cs.MSmath.OC
keywords hdsdpembeddingseveraldualdual-scalingmethodnumericalprogramming
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HDSDP is a numerical software solving the semidefinite programming problems. The main framework of HDSDP resembles the dual-scaling interior point solver DSDP [BY2008] and several new features, including a dual method based on the simplified homogeneous self-dual embedding, have been implemented. The embedding technique enhances stability of the dual method and several new heuristics and computational techniques are designed to accelerate its convergence. HDSDP aims to show how dual-scaling algorithm benefits from the self-dual embedding and it is developed in parallel to DSDP5.8. Numerical experiments over several classical benchmark datasets exhibit its robustness and efficiency, and particularly its advantages on SDP instances featuring low-rank structure and sparsity. HDSDP is open-sourced under MIT license and available at https://github.com/COPT-Public/HDSDP.

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  1. When Does Primal Interior Point Method Beat Primal-dual in Linear Optimization?

    math.OC 2024-11 conditional novelty 6.0 of 10

    A primal interior point method, accelerated by preconditioned iterative solvers, can outperform primal-dual IPM in the final iterations of many linear and semidefinite programs.

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