12 citations · 38 across the 32 of their papers we have counts for
10 papers · 1 filter
Distributionally Robust Optimization over Wasserstein Balls with i.i.d. Structure
Andrey Kharitenko, Marta Fochesato, Anastasios Tsiamis +2
We consider distributionally robust optimization problems where the uncertainty is modeled via a structured Wasserstein ambiguity set. Specifically, the ambiguity is restricted to…
Semismooth Newton Methods for Risk-Averse Markov Decision Processes
Matilde Gargiani, Francesco Micheli, Anastasios Tsiamis +1
Inspired by semismooth Newton methods, we propose a general framework for designing solution methods with convergence guarantees for risk-averse Markov decision processes. Our appr…
Operator Splitting for Convex Constrained Markov Decision Processes
Panagiotis D. Grontas, Anastasios Tsiamis, John Lygeros
We consider finite Markov decision processes (MDPs) with convex constraints and known dynamics. In principle, this problem is amenable to off-the-shelf convex optimization solvers,…
Data-Driven Distributionally Robust System Level Synthesis
Francesco Micheli, Anastasios Tsiamis, John Lygeros
We present a novel approach for the control of uncertain, linear time-invariant systems, which are perturbed by potentially unbounded, additive disturbances. We propose a \emph{dou…
How are policy gradient methods affected by the limits of control?
Ingvar Ziemann, Anastasios Tsiamis, Henrik Sandberg +1
We study stochastic policy gradient methods from the perspective of control-theoretic limitations. Our main result is that ill-conditioned linear systems in the sense of Doyle inev…
State-Output Risk-Constrained Quadratic Control of Partially Observed Linear Systems
Nikolas Koumpis, Anastasios Tsiamis, Dionysios Kalogerias
We propose a methodology for performing risk-averse quadratic regulation of partially observed Linear Time-Invariant (LTI) systems disturbed by process and output noise. To compens…
Linear Quadratic Control with Risk Constraints
Anastasios Tsiamis, Dionysios S. Kalogerias, Alejandro Ribeiro +1
We propose a new risk-constrained formulation of the classical Linear Quadratic (LQ) stochastic control problem for general partially-observed systems. Our framework is motivated b…
Encrypted Distributed Lasso for Sparse Data Predictive Control
Andreea B. Alexandru, Anastasios Tsiamis, George J. Pappas
The least squares problem with L1-regularized regressors, called Lasso, is a widely used approach in optimization problems where sparsity of the regressors is desired. This formula…
Sparse Approximate Solutions to Max-Plus Equations with Application to Multivariate Convex Regression
Nikos Tsilivis, Anastasios Tsiamis, Petros Maragos
In this work, we study the problem of finding approximate, with minimum support set, solutions to matrix max-plus equations, which we call sparse approximate solutions. We show how…
Sparsity in Max-Plus Algebra and Systems
Anastasios Tsiamis, Petros Maragos
We study sparsity in the max-plus algebraic setting. We seek both exact and approximate solutions of the max-plus linear equation with minimum cardinality of support. In the former…