WebIn this work, we consider a block-wise one-sided non-convex min-max problem, in which the minimization problem consists of multiple blocks and is non-convex, while the maximization problem is (strongly) concave. We propose a class of simple algorithms named Hybrid Block Successive Approximation (HiBSA), which alternatingly performs … WebShen, X. Y. Zhang and X. Y. Zhang , A partial PPA block-wise ADMM for multi-block linearly constrained separable convex optimization, Optimization 70(3) (2024) 631–657. Crossref , Google Scholar 33.
Inertial Block Mirror Descent - Optimization Online
WebAbstract. Compressive holography is a relatively time-consuming image estimation in convex optimized problem. We propose an efficient block-wise algorithm to limit the … WebMay 31, 2024 · Then, every limit point of the sequence generated by the block coordinate descent (BCD) method is a stationary point of the original problem. Question. What can we say about the convergence of the block coordinate descent algorithm if either the first or the second conditions above are not satisfied? chase bank research blvd
A partial PPA block-wise ADMM for multi-block linearly …
WebSep 1, 2024 · When the player-specific problems are strongly convex, an inexact pure BR scheme (without a proximal term) is shown to be convergent. In effect, we provide what … WebDec 7, 2024 · Block coordinate descent (BCD), also known as nonlinear Gauss-Seidel, is a simple iterative algorithm for nonconvex optimization that sequentially minimizes the … WebImproving Generalization with Domain Convex Game Fangrui Lv · Jian Liang · Shuang Li · Jinming Zhang · Di Liu SLACK: Stable Learning of Augmentations with Cold-start and KL regularization Juliette Marrie · Michael Arbel · Diane Larlus · Julien Mairal Critical Learning Periods for Multisensory Integration in Deep Networks curtisen as