5 citations · 9 across the 19 of their papers we have counts for
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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…
Optimization of Inf-Convolution Regularized Nonconvex Composite Problems
Emanuel Laude, Tao Wu, Daniel Cremers
In this work, we consider nonconvex composite problems that involve inf-convolution with a Legendre function, which gives rise to an anisotropic generalization of the proximal mapp…