# Constrained Optimization From a Control Perspective via Feedback Linearization

arXiv: https://arxiv.org/abs/2503.12665 · HTML: https://mlsanity.com/paper/2503.12665/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2025.

## Built on by (most cited first, 4 of 4)

- [Safe Online Control-Informed Learning](https://mlsanity.com/paper/2512.13868/) (2025)
- [Physics-Guided Dimension Reduction for Simulation-Free Operator Learning of Stiff Differential-Algebraic Systems](https://mlsanity.com/paper/2604.19930/) (2026)
- [AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training](https://mlsanity.com/paper/2605.08408/) (2026)
- [A Control-Barrier-Function-Based Algorithm for Policy Adaptation in Reinforcement Learning](https://mlsanity.com/paper/2510.02720/) (2025)

## Abstract (arXiv)

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.