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New optimization framework for distributed group-sparse controller design

By PulseAugur Editorial · [1 sources] ·

This paper introduces a unified optimization framework for distributed and sparse feedback linear-quadratic control problems. Researchers propose using the Douglas-Rachford splitting algorithm to solve a nonconvex, nonsmooth optimization problem with an $\ell_0$-penalty. They establish convergence guarantees for this algorithm under specific conditions and also present a projected subgradient descent algorithm that achieves global convergence without these restrictions, potentially serving as a warm-start mechanism for the DR splitting method. Numerical experiments demonstrate the effectiveness of these methods for designing distributed group-sparse controllers. AI

IMPACT Introduces novel optimization techniques that could enhance the design of distributed control systems, potentially impacting areas where AI is applied for complex system management.

RANK_REASON The cluster contains an academic paper detailing a new optimization framework and algorithms for control problems. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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

New optimization framework for distributed group-sparse controller design

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The cluster contains an academic paper detailing a new optimization framework and algorithms for control problems. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Lechen Feng, Xun Li, Yuan-Hua Ni ·

    Douglas-Rachford Splitting for Group-Sparse Feedback Linear-Quadratic Control

    arXiv:2507.19895v4 Announce Type: replace-cross Abstract: In this paper, we study the distributed linear quadratic problem with fixed communication topology (DFT-LQ) and the sparse feedback linear quadratic (SF-LQ) problem through a unified optimization framework. Specifically, b…

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