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math.OC2026

Constrained Flow Matching via Lagrangian Dual Flows

Vince Kurtz, Alexander Davydov

Flow matching is a powerful tool for generative modeling, but emerging applications in robotics, planning, and physics require inference-time constraints on generated outputs. Such…

math.OC2026

Regularized Model Predictive Control via Contractivity and Implicit Lur'e Analysis

Ryotaro Shima, Anand Gokhale, Alexander Davydov +1

This paper develops a contraction-based stability analysis for regularized model predictive control (MPC), whose feedback law is defined implicitly by a finite-horizon optimal cont…

math.OC2025

Non-Euclidean Monotone Operator Theory and Applications

Alexander Davydov, Saber Jafarpour, Anton V. Proskurnikov +1

While monotone operator theory is often studied on Hilbert spaces, many interesting problems in machine learning and optimization arise naturally in finite-dimensional vector space…

math.OC2025

Contractivity Analysis and Control Design for Lur'e Systems: Lipschitz, Incrementally Sector Bounded, and Monotone Nonlinearities

Ryotaro Shima, Alexander Davydov, Francesco Bullo

In this paper, we study the contractivity of Lur'e dynamical systems whose nonlinearity is either Lipschitz, incrementally sector bounded, or monotone. We consider both the discret…

math.OC2025

Time-Varying Convex Optimization: A Contraction and Equilibrium Tracking Approach

Alexander Davydov, Veronica Centorrino, Anand Gokhale +2

In this article, we provide a novel and broadly-applicable contraction-theoretic approach to continuous-time time-varying convex optimization. For any parameter-dependent contracti…

math.OC2024

Proximal Gradient Dynamics: Monotonicity, Exponential Convergence, and Applications

Anand Gokhale, Alexander Davydov, Francesco Bullo

In this letter we study the proximal gradient dynamics. This recently-proposed continuous-time dynamics solves optimization problems whose cost functions are separable into a nonsm…