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.

翻译：HDSDP是一款求解半定规划问题的数值软件。其主框架类似于对偶尺度内点求解器DSDP [BY2008]，并实现了多项新特性，包括基于简化齐次自对偶嵌入的对偶方法。嵌入技术增强了对偶方法的稳定性，同时设计了若干新启发式规则与计算技术以加速其收敛。HDSDP旨在展示自对偶嵌入如何提升对偶尺度算法的性能，其开发与DSDP5.8并行进行。在多个经典基准数据集上的数值实验表明，该软件具有鲁棒性和高效性，尤其在处理具有低秩结构与稀疏性的半定规划实例时优势显著。HDSDP采用MIT许可证开源发布，源代码托管于https://github.com/COPT-Public/HDSDP。