HDSDP: Software for Semidefinite Programming

07/28/2022

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by   Wenzhi Gao, et al.

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Stanford University

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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[2] and several new features, especially a dual method based on the simplified homogeneous self-dual embedding, have been implemented. The embedding enhances stability of dual method and several new heuristics and computational techniques are designed to accelerate its convergence. HDSDP aims to show how dual-scaling algorithms benefit 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. The pre-built binary of HDSDP is currently freely available at https://github.com/COPT-Public/HDSDP.