TR2025-165

Constrained Optimization From a Control Perspective via Feedback Linearization

    •  Zhang, R., Raghunathan, A., Shamma, J., Li, N., "Constrained Optimization From a Control Perspective via Feedback Linearization", The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NuerIPS), December 2025.
      BibTeX TR2025-165 PDF
      • @inproceedings{Zhang2025dec,
      • author = {Zhang, Runyu and Raghunathan, Arvind and Shamma, Jeff and Li, Na},
      • title = {{Constrained Optimization From a Control Perspective via Feedback Linearization}},
      • booktitle = {The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NuerIPS)},
      • year = 2025,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2025-165}
      • }
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  • Research Areas:

    Control, Optimization

Abstract:

Tools from control and dynamical systems have proven valuable for analyzing and developing optimization methods. In this paper, we establish rigorous theoretical foundations for using feedback linearization (FL)—a well-established nonlinear control technique—to solve constrained optimization problems. For equality- constrained optimization, we establish global convergence rates to first-order Karush-Kuhn-Tucker (KKT) points and uncover the close connection between the FL method and the Sequential Quadratic Programming (SQP) algorithm. Building on this relationship, we extend the FL approach to handle inequality-constrained problems. Furthermore, we introduce a momentum-accelerated feedback linearization algorithm and provide a rigorous convergence guarantee.