Primal—dual projected gradient algorithmThis paper shows that the primal-dual steepest descent algorithm developed by Zhu and Rockafellar for large-scale extended linear—quadratic programming can be used in solving constrained minimax problems related to a general C 2 saddle function. It is proved ...
3.1 Conjugate gradient The conjugate gradient method improves the convergence rate of the steepest descent method by choosing descending directions that are a linear combination of the gradient direction with the descending directions of previous iterations. Therefore, their equations are: xk+1=xk+αkdk...
The gradient descent algorithm optimizes the cost function, it is primarily used in Neural Networks for unsupervised learning.
Some third parties are outside of the European Economic Area, with varying standards of data protection. See our privacy policy for more information on the use of your personal data. Manage preferences for further information and to change your choices. Accept all cookies ...
内容提示: Stochastic Gradient Descent Jittering for Inverse Problems:Alleviating the Accuracy-Robustness TradeoffPeimeng Guan1 , Mark A. Davenport 1Georgia Intitute of TechnologyAtlanta, GA 30332 USA{pguan6, mdav}@gatech.eduAbstractInverse problems aim to reconstruct unseen data from cor-rupted or ...
The former is a version of stochastic gradient descent with with adaptive moment estimation, see Kingma and Ba [19] and the latter is a quasi-Newton method, see Liu and Nocedal [22]. Typically, the optimization process begins with applying Adam until convergence slows down and the fast local...
(cf. Eq.3) for the correct, yielded by the PDE, and approximate fluid-flow physics, yielded by the trained FNO. The results of these inversions after 100 iterations of gradient descent with back-tracking linesearch [87] are plotted in Fig.8a and b. From these plots, we observe that ...
In the paper, we study a class of useful minimax problems on Riemanian manifolds and propose a class of effective Riemanian gradient-based methods to solve these minimax problems. Specifically, we propose an effective Riemannian gradient descent ascent (RGDA) algorithm for the deterministic minimax...
A few efforts have been made to solve decentralized nonconvex strongly-concave (NCSC) minimax-structured optimization; however, all of them focus on smooth problems with at most a constraint on the maximization variable. In this paper, we make the first attempt on solving composite NCSC minimax ...
Paper tables with annotated results for Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems