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Researchers develop learned policies for ADMM to improve optimization performance

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a method to learn online updates for the relaxation parameter in the Alternating Direction Method of Multipliers (ADMM). This approach aims to improve the performance of ADMM, a technique used in structured convex optimization, by adapting parameters for specific problem classes. The learned policies have demonstrated improvements in both iteration count and execution time on benchmark quadratic programs compared to standard methods. AI

IMPACT Potential for faster, more efficient optimization in AI model training and deployment.

RANK_REASON Academic paper on optimization techniques.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Researchers develop learned policies for ADMM to improve optimization performance

COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Junan Lin, Paul J. Goulart, Luca Furieri ·

    Learning Over-Relaxation Policies for ADMM with Convergence Guarantees

    arXiv:2604.26932v1 Announce Type: cross Abstract: The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depends strongly on the choice of penalty and relaxation parameters. Motivated by set…

  2. arXiv cs.LG TIER_1 English(EN) · Luca Furieri ·

    Learning Over-Relaxation Policies for ADMM with Convergence Guarantees

    The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depends strongly on the choice of penalty and relaxation parameters. Motivated by settings such as Model Predictive Control (MPC), wher…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Learning Over-Relaxation Policies for ADMM with Convergence Guarantees

    The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depends strongly on the choice of penalty and relaxation parameters. Motivated by settings such as Model Predictive Control (MPC), wher…

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