125 citations · 985 across the 106 of their papers we have counts for
119 papers
A Smoothed Augmented Lagrangian Method for Mathematical Programs with Vertical Complementarity Constraints
Jia Wang, Andreas Themelis, Panagiotis Patrinos
We propose a smoothed augmented Lagrangian method (ALM) for solving mathematical programs with vertical complementarity constraints (VCCs). The augmented Lagrangian function involv…
Active learning-based Bayesian optimization in the realm of copper slag-blended cement systems
Debadri Som, Rayna Maheshwari, Mathijs Schuurmans +2
Accelerated mix design optimization is critical for deploying low-carbon supplementary cementitious materials (SCMs) because traditional experimental approaches require extensive t…
Newton methods beyond Hessian Lipschitz continuity: A nonlinear preconditioning approach
Alexander Bodard, Panagiotis Patrinos
Newton-type methods are typically analyzed under Lipschitz continuity of the Hessian, an assumption that can fail for objectives with higher-order or polynomial growth. We introduc…
Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives
Konstantinos Oikonomidis, Jan Quan, Kimon Antonakopoulos +3
In this work, we develop proximal preconditioned gradient methods with a focus on spectral gradient methods providing a proximal extension to the Muon and Scion optimizers. We intr…
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…
Probabilistic Safety under Arbitrary Disturbance Distributions using Piecewise-Affine Control Barrier Functions
Matisse Teuwen, Mathijs Schuurmans, Panagiotis Patrinos
We propose a simple safety filter design for stochastic discrete-time systems based on piecewise affine probabilistic control barrier functions, providing an appealing balance betw…
Scaled relative graphs for pairs of operators beyond classical monotonicity
Jan Quan, Alexander Bodard, Konstantinos Oikonomidis +1
We introduce a generalization of the scaled relative graph (SRG) to pairs of operators, enabling the visualization of their relative incremental properties. This novel SRG framewor…
A Lasry-Lions envelope approach for mathematical programs with complementarity constraints
Jia Wang, Andreas Themelis, Ivan Markovsky +1
We propose a homotopy method for solving mathematical programs with complementarity constraints (CCs). The indicator function of the CCs is relaxed by the Lasry--Lions double envel…
Tight Convergence Rates in Gradient Mapping for the Difference-of-Convex Algorithm
Teodor Rotaru, Panagiotis Patrinos, François Glineur
We establish new theoretical convergence guarantees for the difference-of-convex algorithm (DCA), where the second function is allowed to be weakly-convex, measuring progress via c…
A localized consensus-based sampling algorithm
Arne Bouillon, Alexander Bodard, Panagiotis Patrinos +2
We propose a localized consensus-based method for sampling from non-Gaussian distributions, a task that frequently arises when solving Bayesian inverse problems. Our method arises…
Spingarn's Method and Progressive Decoupling Beyond Elicitable Monotonicity
Brecht Evens, Puya Latafat, Panagiotis Patrinos
Spingarn's method of partial inverses and the progressive decoupling algorithm address inclusion problems involving the sum of an operator and the normal cone of a linear subspace,…
Forward-backward splitting under the light of generalized convexity
Konstantinos Oikonomidis, Emanuel Laude, Panagiotis Patrinos
In this paper we present a unifying framework for continuous optimization methods grounded in the concept of generalized convexity. Utilizing the powerful theory of -convexity,…
Tight Analysis of Difference-of-Convex Algorithm (DCA) Improves Convergence Rates for Proximal Gradient Descent
Teodor Rotaru, Panagiotis Patrinos, François Glineur
We investigate a difference-of-convex (DC) formulation where the second term is allowed to be weakly convex. We examine the precise behavior of a single iteration of the difference…
Risk-Sensitive Model Predictive Control for Interaction-Aware Planning -- A Sequential Convexification Algorithm
Renzi Wang, Mathijs Schuurmans, Panagiotis Patrinos
This paper considers risk-sensitive model predictive control for stochastic systems with a decision-dependent distribution. This class of systems is commonly found in human-robot i…
Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness
Konstantinos Oikonomidis, Jan Quan, Emanuel Laude +1
We analyze nonlinearly preconditioned gradient methods for solving smooth minimization problems. We introduce a generalized smoothness property, based on the notion of abstract con…
An Efficient Mixed-Integer Formulation and an Iterative Method for Optimal Control of Switched Systems Under Dwell Time Constraints
Ramin Abbasi-Esfeden, Armin Nurkanovic, Moritz Diehl +2
This paper presents an efficient Mixed-Integer Nonlinear Programming (MINLP) formulation for systems with discrete control inputs under dwell time constraints. By viewing such syst…
Scaled Relative Graphs for Nonmonotone Operators with Applications in Circuit Theory
Jan Quan, Brecht Evens, Rodolphe Sepulchre +1
The scaled relative graph (SRG) is a powerful graphical tool for analyzing the properties of operators, by mapping their graph onto the complex plane. In this work, we study the SR…
The inexact power augmented Lagrangian method for constrained nonconvex optimization
Alexander Bodard, Konstantinos Oikonomidis, Emanuel Laude +1
This work introduces an unconventional inexact augmented Lagrangian method where the augmenting term is a Euclidean norm raised to a power between one and two. The proposed algorit…
Stability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex Optimization Problems
Ibrahim K. Ozaslan, Panagiotis Patrinos, Mihailo R. Jovanović
We examine stability properties of primal-dual gradient flow dynamics for composite convex optimization problems with multiple, possibly nonsmooth, terms in the objective function…
Quantization-aware Matrix Factorization for Low Bit Rate Image Compression
Pooya Ashtari, Pourya Behmandpoor, Fateme Nateghi Haredasht +3
Lossy image compression is essential for efficient transmission and storage. Traditional compression methods mainly rely on discrete cosine transform (DCT) or singular value decomp…
EM++: A parameter learning framework for stochastic switching systems
Renzi Wang, Alexander Bodard, Mathijs Schuurmans +1
This paper proposes a general switching dynamical system model, and a custom majorization-minimization-based algorithm EM++ for identifying its parameters. For certain families of…
Exact worst-case convergence rates of gradient descent: a complete analysis for all constant stepsizes over nonconvex and convex functions
Teodor Rotaru, François Glineur, Panagiotis Patrinos
We consider gradient descent with constant stepsizes and derive exact worst-case convergence rates on the minimum gradient norm of the iterates. Our analysis covers all possible st…
Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method
Qinghua Tao, Francesco Tonin, Alex Lambert +3
In contrast with Mercer kernel-based approaches as used e.g., in Kernel Principal Component Analysis (KPCA), it was previously shown that Singular Value Decomposition (SVD) inheren…
Improved convergence rates for the Difference-of-Convex algorithm
Teodor Rotaru, Panagiotis Patrinos, François Glineur
We consider a difference-of-convex formulation where one of the terms is allowed to be hypoconvex (or weakly convex). We first examine the precise behavior of a single iteration of…