277 citations · 1.6k across the 89 of their papers we have counts for
5 papers · 2 filters
On Convex Data-Driven Inverse Optimal Control for Nonlinear, Non-stationary and Stochastic Systems
Emiland Garrabe, Hozefa Jesawada, Carmen Del Vecchio +1
This paper is concerned with a finite-horizon inverse control problem, which has the goal of reconstructing, from observations, the possibly non-convex and non-stationary cost driv…
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…
On a Probabilistic Approach for Inverse Data-Driven Optimal Control
Émiland Garrabé, Hozefa Jesawada, Carmen Del Vecchio +1
We consider the problem of estimating the possibly non-convex cost of an agent by observing its interactions with a nonlinear, non-stationary and stochastic environment. For this i…
Optimal Decision-Making for Autonomous Agents via Data Composition
Emiland Garrabe, Martina Lamberti, Giovanni Russo
We consider the problem of designing agents able to compute optimal decisions by composing data from multiple sources to tackle tasks involving: (i) tracking a desired behavior whi…
Euclidean Contractivity of Neural Networks with Symmetric Weights
Veronica Centorrino, Anand Gokhale, Alexander Davydov +2
This paper investigates stability conditions of continuous-time Hopfield and firing-rate neural networks by leveraging contraction theory. First, we present a number of useful gene…