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New SDP solver SDPLR+ scales to million-variable problems

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SDP solver SDPLR+ scales to million-variable problems

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

  1. arXiv cs.LG TIER_1 English(EN) · Yufan Huang, David F. Gleich ·

    Suboptimality bounds for trace-bounded SDPs enable a faster and scalable low-rank SDP solver SDPLR+

    arXiv:2406.10407v3 Announce Type: replace-cross Abstract: Semidefinite programs (SDPs) and their solvers are powerful tools with many applications in machine learning and data science. Designing scalable SDP solvers is challenging because by standard the positive semidefinite dec…

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