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02/15/2023
An abstract convergence framework with application to inertial inexact forward–backward methods
In this paper we introduce a novel abstract descent scheme suited for th...
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10/20/2022
PAC-Bayesian Learning of Optimization Algorithms
We apply the PAC-Bayes theory to the setting of learning-to-optimize. To...
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08/05/2022
Fixed-Point Automatic Differentiation of Forward–Backward Splitting Algorithms for Partly Smooth Functions
A large class of non-smooth practical optimization problems can be writt...
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12/24/2020
Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms
Lipschitz continuity of the gradient mapping of a continuously different...
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08/18/2020
Self-supervised Sparse to Dense Motion Segmentation
Observable motion in videos can give rise to the definition of objects m...
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10/08/2019
Bregman Proximal Framework for Deep Linear Neural Networks
A typical assumption for the analysis of first order optimization method...
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05/22/2019
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms
Matrix Factorization is a popular non-convex objective, for which altern...
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04/06/2019
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization
Backtracking line-search is an old yet powerful strategy for finding bet...
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01/23/2019
Model Function Based Conditional Gradient Method with Armijo-like Line Search
The Conditional Gradient Method is generalized to a class of non-smooth ...
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03/23/2018
Lifting Layers: Analysis and Applications
The great advances of learning-based approaches in image processing and ...
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07/07/2017
Non-smooth Non-convex Bregman Minimization: Unification and new Algorithms
We propose a unifying algorithm for non-smooth non-convex optimization. ...
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04/18/2014