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Bregman Proximal Gradient Algorithms for Deep Matrix Factorization., , , , and . SSVM, volume 12679 of Lecture Notes in Computer Science, page 204-215. Springer, (2021)Adaptive proximal gradient methods are universal without approximation., , , , and . CoRR, (2024)Distributed Photometric Bundle Adjustment., , , , and . 3DV, page 140-149. IEEE, (2020)Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields., , , , and . SIAM J. Imaging Sci., 15 (3): 1253-1281 (September 2022)Dualities for Non-Euclidean Smoothness and Strong Convexity under the Light of Generalized Conjugacy., , and . SIAM J. Optim., 33 (4): 2721-2749 (December 2023)Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields., , , , and . CoRR, (2021)Bregman Proximal Mappings and Bregman-Moreau Envelopes Under Relative Prox-Regularity., , and . J. Optim. Theory Appl., 184 (3): 724-761 (2020)Optimization of Inf-Convolution Regularized Nonconvex Composite Problems., , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 547-556. PMLR, (2019)Bregman Proximal Framework for Deep Linear Neural Networks., , , , and . CoRR, (2019)