15 citations · 55 across the 17 of their papers we have counts for
24 papers
Euclidean Distance-Based Flow Matching
Paul-Henrik Heilmann, Peter Ochs
Flow Matching has recently become popular as a technique for generative modeling with applications in many domains. In the standard Flow Matching formulation, we interpret the vect…
A Quasi-Newton Primal-Dual Algorithm with Line Search
Shida Wang, Jalal Fadili, Peter Ochs
Quasi-Newton methods refer to a class of algorithms at the interface between first and second order methods. They aim to progress as substantially as second order methods per itera…
An abstract convergence framework with application to inertial inexact forward--backward methods
Silvia Bonettini, Peter Ochs, Marco Prato +1
In this paper we introduce a novel abstract descent scheme suited for the minimization of proper and lower semicontinuous functions. The proposed abstract scheme generalizes a set…
Continuous Newton-like Methods featuring Inertia and Variable Mass
Camille Castera, Hedy Attouch, Jalal Fadili +1
We introduce a new dynamical system, at the interface between second-order dynamics with inertia and Newton's method. This system extends the class of inertial Newton-like dynamics…
PAC-Bayesian Learning of Optimization Algorithms
Michael Sucker, Peter Ochs
We apply the PAC-Bayes theory to the setting of learning-to-optimize. To the best of our knowledge, we present the first framework to learn optimization algorithms with provable ge…
Inertial Quasi-Newton Methods for Monotone Inclusion: Efficient Resolvent Calculus and Primal-Dual Methods
Shida Wang, Jalal Fadili, Peter Ochs
We introduce an inertial quasi-Newton Forward-Backward Splitting Algorithm to solve a class of monotone inclusion problems. While the inertial step is computationally cheap, in gen…