6 citations · 16 across the 31 of their papers we have counts for
12 papers · 1 filter
Learning Parametric Monotone Games
Alberto Bemporad, Tatiana Tatarenko
We study the problem of learning from data a parametric Nash equilibrium (NE) problem that is monotone (or strongly monotone) for all parameter values. In the presence of local and…
Solving Monotone Linear-Quadratic Generalized Nash Equilibrium Problems via Quadratic Programming
Alberto Bemporad, Tatiana Tatarenko
We consider generalized Nash equilibrium problems among players with convex quadratic costs and shared affine constraints, assuming only that the game's pseudogradient is merel…
Learning Approximate Solutions to Multiparametric Generalized Nash Equilibrium Problems
A. Bemporad, T. Tatarenko
We propose a learning-based approach for approximating solution mappings of multiparametric generalized Nash equilibrium problems (GNEPs) with coupling in both objectives and const…
Towards A Goldfarb-Idnani Variant for Strongly Monotone Linear-Quadratic Games
Alberto Bemporad
We analyze a simple variant of the Goldfarb-Idnani (GI) dual active-set method for computing variational generalized Nash equilibria of strongly monotone N-player games with convex…
Parametric Nonconvex Optimization via Convex Surrogates
Renzi Wang, Panagiotis Patrinos, Alberto Bemporad
This paper presents a novel learning-based approach to construct a surrogate problem that approximates a given parametric nonconvex optimization problem. The surrogate function is…
A proximal augmented Lagrangian method for nonconvex optimization with equality and inequality constraints
Adeyemi D. Adeoye, Puya Latafat, Alberto Bemporad
We propose an inexact proximal augmented Lagrangian method (P-ALM) for nonconvex structured optimization problems. The proposed method features an easily implementable rule not onl…
Automatic Generation of Explicit Quadratic Programming Solvers
Maximilian Schaller, Daniel Arnström, Alberto Bemporad +1
We consider a family of convex quadratic programs in which the coefficients of the linear objective term and the righthand side of the constraints are affine functions of a paramet…
Learning Parametric Convex Functions
Maximilian Schaller, Alberto Bemporad, Stephen Boyd
A parametrized convex function depends on a variable and a parameter, and is convex in the variable for any valid value of the parameter. Such functions can be used to specify para…
Learning Quasi-LPV Models and Robust Control Invariant Sets with Reduced Conservativeness
Sampath Kumar Mulagaleti, Alberto Bemporad
We present an approach to identify a quasi Linear Parameter Varying (qLPV) model of a plant, with the qLPV model guaranteed to admit a robust control invariant (RCI) set. It builds…
Efficient identification of linear, parameter-varying, and nonlinear systems with noise models
Alberto Bemporad, Roland Tóth
We present a general system identification procedure capable of estimating of a broad spectrum of state-space dynamical models, including linear time-invariant (LTI), linear parame…
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control
Andrea Ghezzi, Rudolf Reiter, Katrin Baumgärtner +2
We propose a computationally efficient rollout-then-optimize method to improve a learned control policy at deployment time. A learned policy provides a nominal trajectory, which is…
Actively learning equilibria in Nash games with misleading information
Barbara Franci, Filippo Fabiani, Alberto Bemporad
We develop a scheme based on active learning to compute equilibria in a generalized Nash equilibrium problem (GNEP). Specifically, an external observer (or entity), with little kno…