6 citations · 16 across the 31 of their papers we have counts for
33 papers
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 practically stabilizing output-feedback nonlinear controllers
Kui Xie, Pablo Krupa, Alberto Bemporad
This paper addresses the problem of learning an output-feedback surrogate controller offline that approximates a given, possibly computationally expensive, nonlinear controller-obs…
Amortized Nonlinear Model Predictive Control
Francesco Pillitteri, Alberto Bemporad
Nonlinear Model Predictive Control requires solving a constrained nonlinear program (NLP) in real-time at every sampling instant, a computational bottleneck that limits deployment…
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…
Active Learning MPC Objective Functions from Preferences
Hasna El Hasnaouy, Pablo Krupa, Mario Zanon +1
Designing the objective function in Model Predictive Control (MPC) is challenging when performance assessment criteria are available only from human judgment. We adopt a preference…
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…
Learning generalized Nash equilibria from pairwise preferences
Pablo Krupa, Alberto Bemporad
Generalized Nash Equilibrium Problems (GNEPs) arise in many applications, including non-cooperative multi-agent control problems. Although many methods exist for finding generalize…
Worst-case Nonlinear Regression with Error Bounds
Alberto Bemporad
We propose an active-learning method for nonlinear minimax regression. Given a nonlinear function that can be arbitrarily evaluated over a compact set, we fit a surrogate model, su…
Configuration-Constrained Tube MPC for Periodic Operation
Filippo Badalamenti, Jose A. Borja-Conde, Sampath Kumar Mulagaleti +3
Periodic operation often emerges as the economically optimal mode in industrial processes, particularly under varying economic or environmental conditions. This paper proposes a ro…
A Regularization and Active Learning Method for Identification of Quasi Linear Parameter Varying Systems
Sampath Kumar Mulagaleti, Alberto Bemporad
This paper proposes an active learning method for designing experiments to identify quasi-Linear Parameter-Varying (qLPV) models. Since informative experiments are costly, input si…
NashOpt - A Python Library for Computing Generalized Nash Equilibria
Alberto Bemporad
NashOpt is an open-source Python library for computing and designing generalized Nash equilibria (GNEs) in noncooperative games with shared constraints and real-valued decision var…
Solving Multiparametric Generalized Nash Equilibrium Problems and Explicit Game-Theoretic Model Predictive Control
Sophie Hall, Alberto Bemporad
We present a method for computing explicit solutions to parametric generalized Nash equilibrium (GNE) problems with convex quadratic cost functions and linear coupling and local co…
Learning the MPC objective function from human preferences
Pablo Krupa, Hasna El Hasnaouy, Mario Zanon +1
In Model Predictive Control (MPC), the objective function plays a central role in determining the closed-loop behavior of the system, and must therefore be designed to achieve the…
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…
Online design of experiments by active learning for nonlinear system identification
Kui Xie, Alberto Bemporad
We investigate the use of active-learning (AL) strategies to generate the input excitation signal at runtime for system identification of linear and nonlinear autoregressive and st…
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…
Efficient Configuration-Constrained Tube MPC via Variables Restriction and Template Selection
Filippo Badalamenti, Sampath Kumar Mulagaleti, Mario Eduardo Villanueva +2
Configuration-Constrained Tube Model Predictive Control (CCTMPC) offers flexibility by using a polytopic parameterization of invariant sets and the optimization of an associated ve…
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…
Active Learning-Based Input Design for Angle-Only Initial Relative Orbit Determination
Kui Xie, Giovanni Romagnoli, Giordana Bucchioni +1
Accurate relative orbit determination is a significant challenge in modern space operations, particularly when relying only on angular measurements. The inherent observability limi…
Learning Low-Dimensional Embeddings for Black-Box Optimization
Riccardo Busetto, Manas Mejari, Marco Forgione +2
When gradient-based methods are impractical, black-box optimization (BBO) provides a valuable alternative. However, BBO often struggles with high-dimensional problems and limited t…
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…