20 citations · 29 across the 6 of their papers we have counts for
7 papers
Modified Bryson-Frazier Smoothing and Hyperparameter Learning for Temporal Gaussian Process Regression
Tom Colemont, Brecht Evens, Tjonnie G. F. Li +1
One-dimensional Gaussian processes with stationary, integrable kernel functions admit exact or arbitrarily accurate state-space representations, enabling linear-time inference thro…
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,…
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…
Convergence of the Chambolle-Pock Algorithm in the Absence of Monotonicity
Brecht Evens, Puya Latafat, Panagiotis Patrinos
The Chambolle-Pock algorithm (CPA), also known as the primal-dual hybrid gradient method, has gained popularity over the last decade due to its success in solving large-scale conve…
Convergence of the Preconditioned Proximal Point Method and Douglas-Rachford Splitting in the Absence of Monotonicity
Brecht Evens, Pieter Pas, Puya Latafat +1
The proximal point algorithm (PPA) is the most widely recognized method for solving inclusion problems and serves as the foundation for many numerical algorithms. Despite this popu…
Learning MPC for Interaction-Aware Autonomous Driving: A Game-Theoretic Approach
Brecht Evens, Mathijs Schuurmans, Panagiotis Patrinos
We consider the problem of interaction-aware motion planning for automated vehicles in general traffic situations. We model the interaction between the controlled vehicle and surro…
Neural Network Training as an Optimal Control Problem: An Augmented Lagrangian Approach
Brecht Evens, Puya Latafat, Andreas Themelis +2
Training of neural networks amounts to nonconvex optimization problems that are typically solved by using backpropagation and (variants of) stochastic gradient descent. In this wor…