16 citations · 17 across the 6 of their papers we have counts for
6 papers
A model-free first-order method for linear quadratic regulator with sampling complexity
Caleb Ju, Georgios Kotsalis, Guanghui Lan
We consider the classic stochastic linear quadratic regulator (LQR) problem under an infinite horizon average stage cost. By leveraging recent policy gradient methods from reinforc…
Tight Computationally Efficient Approximation of Matrix Norms with Applications
Anatoli Juditsky, Georgios Kotsalis, Arkadi Nemirovski
We address the problems of computing operator norms of matrices induced by given norms on the argument and the image space. It is known that aside of a fistful of "solvable cases,"…
Simple and optimal methods for stochastic variational inequalities, II: Markovian noise and policy evaluation in reinforcement learning
Georgios Kotsalis, Guanghui Lan, Tianjiao Li
The focus of this paper is on stochastic variational inequalities (VI) under Markovian noise. A prominent application of our algorithmic developments is the stochastic policy evalu…
Simple and optimal methods for stochastic variational inequalities, I: operator extrapolation
Georgios Kotsalis, Guanghui Lan, Tianjiao Li
In this paper we first present a novel operator extrapolation (OE) method for solving deterministic variational inequality (VI) problems. Similar to the gradient (operator) project…
Convex optimization for finite horizon robust covariance control of linear stochastic systems
Georgios Kotsalis, Guanghui Lan, Arkadi Nemirovski
This work addresses the finite-horizon robust covariance control problem for discrete-time, partially observable, linear system affected by random zero mean noise and deterministic…
Robust affine control of linear stochastic systems
Georgios Kotsalis, Guanghui Lan
In this work we provide a computationally tractable procedure for designing affine control policies, applied to constrained, discrete-time, partially observable, linear systems sub…