110 citations · 203 across the 7 of their papers we have counts for
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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…
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…
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…
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization
Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock +1
Backtracking line-search is an old yet powerful strategy for finding a better step sizes to be used in proximal gradient algorithms. The main principle is to locally find a simple…