activity
20102026
most citedNon-Euclidean Contraction Theory for Robust Nonlinear Stability

52 citations · 111 across the 30 of their papers we have counts for

collaborators

30 papers

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 control 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…

eess.SY2026

Learning Certified Neural Network Controllers Using Contraction and Interval Analysis

Akash Harapanahalli, Samuel Coogan, Alexander Davydov

We present a novel framework that jointly trains a neural network controller and a neural Riemannian metric with rigorous closed-loop contraction guarantees using formal bound prop…

q-bio.NC2025

Competition, stability, and functionality in excitatory-inhibitory neural circuits

Simone Betteti, William Retnaraj, Alexander Davydov +2

Energy-based models have become a central paradigm for understanding computation and stability in both theoretical neuroscience and machine learning. However, the energetic framewo…

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.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…