WebMar 15, 2024 · In this paper, we introduce three new inertial-like Bregman projection methods with a nonmonotone adaptive step-size for solving quasi-monotone variational … WebJun 23, 2024 · In the paper, we design a novel Bregman gradient policy optimization framework for reinforcement learning based on Bregman divergences and momentum …
A variational perspective on accelerated methods in …
WebBregman proximal point optimization, which is an instance of trust-region methods and can prevent aggressive updating. Our experiments show that the proposed framework achieves new state-of-the-art performance on a number of NLP tasks including GLUE, SNLI, SciTail and ANLI. Moreover, it also outperforms the http://sharky93.github.io/docs/dev/api/skimage.restoration.html parimatch login india
New Bregman proximal type algorithms for solving DC optimization …
WebBacktracking line-search is an old yet powerful strategy for finding better step sizes to be used in proximal gradient algorithms. The main principle is to locally find a simple convex upper bound of the objective function, which in turn controls the step size that is used. In case of inertial proximal gradient algorithms, the situation becomes much more difficult … The Bregman method is an iterative algorithm to solve certain convex optimization problems involving regularization. The original version is due to Lev M. Bregman, who published it in 1967. The algorithm is a row-action method accessing constraint functions one by one and the method is particularly suited for … See more In order to be able to use the Bregman method, one must frame the problem of interest as finding $${\displaystyle \min _{u}J(u)+f(u)}$$, where $${\displaystyle J}$$ is a regularizing function such as The Bregman … See more The Bregman method or its generalizations can be applied to: • Image deblurring or denoising (including total variation denoising See more The method has links to the method of multipliers and dual ascent method (through the so-called Bregman alternating direction method of multipliers, generalizing the … See more WebIn this article, we propose the Bregman Proximal DC Algorithm (BPDCA) for solving large-scale DC optimization problems that do not possess L -smoothness. Instead, it requires that the convex part of the objective function has the L -smooth adaptable property that is exploited in Bregman proximal gradient algorithms. parimatch casino 20fs