Code and Data Repository for A Low-Rank ADMM Splitting Approach for Semidefinite Programming

Qiushi Han; Chenxi Li; Zhenwei Lin; Caihua Chen; Qi Deng; Dongdong Ge; Huikang Liu; Yinyu Ye

doi:10.1287/ijoc.2024.0701.cd

August 5, 2025INFORMS journal on computing

Code and Data Repository for A Low-Rank ADMM Splitting Approach for Semidefinite Programming

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Key Points

MAIN FINDING: LoRADS is an enhanced solver for low rank semidefinite programming (SDP) problems.
KEY EVIDENCE: The algorithm significantly optimizes solutions in its class, showcasing improved performance.
APPROACH: Written in ANSI C, LoRADS employs a first-order method for solving SDPs effectively.
SIGNIFICANCE: This tool supports ongoing research by providing a robust framework for complex optimization challenges.

Implication

New low rank solver improves efficiency in semidefinite programming, suggesting advancements in optimization techniques.

Abstract

LoRADS is an enhanced first-order method solver for low rank Semi-definite programming problems (SDPs). LoRADS is written in ANSI C and is maintained by Cardinal Operations COPT development team. More features are still under active development.

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Cite This Study

Han et al. (2025) studied this question.

synapsesocial.com/papers/689521e49f4f1c896c428256 https://doi.org/10.1287/ijoc.2024.0701.cd

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Code and Data Repository for A Low-Rank ADMM Splitting Approach for Semidefinite Programming

Key Points

Implication

Abstract

Discussion

Cite This Study

Code and Data Repository for A Low-Rank ADMM Splitting Approach for Semidefinite Programming

Key Points

Implication

Abstract

Discussion

Cite This Study

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