Designing Poisson Integrators through Machine Learning
This paper presents a general method to construct Poisson integrators, i.e., integrators that preserve the underlying Poisson geometry. We assume the Poisson manifold is …
Data-Driven Synthesis of Optimization-Based Controllers for Regulation of Unknown Linear Systems
This paper proposes a data-driven framework to solve time-varying optimization problems associated with unknown linear dynamical systems. Making online control decisions to …
g.-bianchin
Convergence-Rate-Matching Discretization of Accelerated Optimization Flows through Opportunistic State-Triggered Control
A recent body of exciting work seeks to shed light on the behavior of accelerated methods in optimization via high-resolution differential equations. These differential equations …
Distributed Augmentation-Regularization for Robust Online Convex Optimization
This paper studies the use of distributed, primal-dual dynamics to solve continuous, time-dependent optimization problems on the fly. When using primal-dual dynamics, the …