5 citations · 10 across the 5 of their papers we have counts for
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Differentiating the Value Function by using Convex Duality
Sheheryar Mehmood, Peter Ochs
We consider the differentiation of the value function for parametric optimization problems. Such problems are ubiquitous in Machine Learning applications such as structured support…
Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms
Mahesh Chandra Mukkamala, Jalal Fadili, Peter Ochs
Lipschitz continuity of the gradient mapping of a continuously differentiable function plays a crucial role in designing various optimization algorithms. However, many functions ar…
Automatic Differentiation of Some First-Order Methods in Parametric Optimization
Sheheryar Mehmood, Peter Ochs
We aim at computing the derivative of the solution to a parametric optimization problem with respect to the involved parameters. For a class broader than that of strongly convex fu…
Bregman Proximal Framework for Deep Linear Neural Networks
Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude +2
A typical assumption for the analysis of first order optimization methods is the Lipschitz continuity of the gradient of the objective function. However, for many practical applica…
Bregman Proximal Mappings and Bregman-Moreau Envelopes under Relative Prox-Regularity
Emanuel Laude, Peter Ochs, Daniel Cremers
We systematically study the local single-valuedness of the Bregman proximal mapping and local smoothness of the Bregman--Moreau envelope of a nonconvex function under relative prox…
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms
Mahesh Chandra Mukkamala, Peter Ochs
Matrix Factorization is a popular non-convex optimization problem, for which alternating minimization schemes are mostly used. They usually suffer from the major drawback that the…