New SDP solver SDPLR+ scales to million-variable problems

Researchers have developed a new semidefinite programming (SDP) solver called SDPLR+ that significantly improves scalability and speed for large-scale problems. This solver optimizes over low-rank factorizations, reducing storage costs compared to traditional methods. SDPLR+ dynamically adjusts the rank during optimization, enabling early termination and faster computation, and has demonstrated effectiveness on problems with up to million-by-million decision variables. AI

IMPACT Enhances computational capabilities for machine learning and data science problems involving large-scale optimization.

RANK_REASON The cluster contains a research paper detailing a new algorithm and solver. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Barvinok and Pataki
• Burer and Monteiro
• data science
• machine learning
• Minimum Bisection
• SDPLR+
• Semidefinite programs (SDPs)

• paper
• other

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