5 citations · 8 across the 8 of their papers we have counts for
8 papers
Online Feedback Optimization for Constrained Stochastic Problems with Decision-Dependent Distributions
Caio Kalil Lauand, Emiliano Dall'Anese
Online feedback optimization (OFO) leverages real-time output measurements to optimize the operation of networked systems without requiring full knowledge of system dynamics or dis…
Global Convergence and Acceleration for Single Observation Gradient Free Optimization
Caio Kalil Lauand, Sean Meyn
Simultaneous perturbation stochastic approximation (SPSA) is an approach to gradient-free optimization introduced by Spall as a simplification of the approach of Kiefer and Wolfowi…
Stochastic Online Feedback Optimization for Networks of Non-Compliant Agents
Caio Kalil Lauand, Andrey Bernstein
In several applications of online optimization to networked systems such as power grids and robotic networks, information about the system model and its disturbances is not general…
Markovian Foundations for Quasi-Stochastic Approximation in Two Timescales: Extended Version
Caio Kalil Lauand, Sean Meyn
Many machine learning and optimization algorithms can be cast as instances of stochastic approximation (SA). The convergence rate of these algorithms is known to be slow, with the…
Revisiting Step-Size Assumptions in Stochastic Approximation
Caio Kalil Lauand, Sean Meyn
Many machine learning and optimization algorithms are built upon the framework of stochastic approximation (SA), for which the selection of step-size (or learning rate) i…
The case for and against fixed step-size: Stochastic approximation algorithms in optimization and machine learning
Caio Kalil Lauand, Ioannis Kontoyiannis, Sean Meyn
Theory and application of stochastic approximation (SA) have become increasingly relevant due in part to applications in optimization and reinforcement learning. This paper takes a…
Markovian Foundations for Quasi-Stochastic Approximation with Applications to Extremum Seeking Control
Caio Kalil Lauand, Sean Meyn
This paper concerns quasi-stochastic approximation (QSA) to solve root finding problems commonly found in applications to optimization and reinforcement learning. The general const…
Extremely Fast Convergence Rates for Extremum Seeking Control with Polyak-Ruppert Averaging
Caio Kalil Lauand, Sean Meyn
Stochastic approximation is a foundation for many algorithms found in machine learning and optimization. It is in general slow to converge: the mean square error vanishes as $O(n^{…