Solving a class of non-convex min-max games using adaptive momentum methods

• URL: http://arxiv.org/abs/2104.12676v1
• Date: Mon, 26 Apr 2021 16:06:39 GMT
• Title: Solving a class of non-convex min-max games using adaptive momentum methods
• Authors: Babak Barazandeh, Davoud Ataee Tarzanagh, George Michailidis
• Abstract summary: Adaptive momentum methods have attracted a lot of attention for deep neural networks. In this paper, we propose an adaptive momentum min-max optimization problem in adversarial networks. Experimental results illustrate its superior performance vis-avis methods for solving such problems.
• Score: 9.538456363995161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive momentum methods have recently attracted a lot of attention for training of deep neural networks. They use an exponential moving average of past gradients of the objective function to update both search directions and learning rates. However, these methods are not suited for solving min-max optimization problems that arise in training generative adversarial networks. In this paper, we propose an adaptive momentum min-max algorithm that generalizes adaptive momentum methods to the non-convex min-max regime. Further, we establish non-asymptotic rates of convergence for the proposed algorithm when used in a reasonably broad class of non-convex min-max optimization problems. Experimental results illustrate its superior performance vis-a-vis benchmark methods for solving such problems.

Related papers

• Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems [0.5156484100374059]
  We explore the relationship between recent line search approaches for deep optimization in the overparametrized regime and momentum directions. We introduce algorithmic that exploits a mix of data persistency, conjugateient type rules for the definition of the momentum parameter. The resulting algorithm is empirically shown to outperform other popular methods.
  arXiv  Detail & Related papers  (2024-11-11T16:26:33Z)
• Enhancing CNN Classification with Lamarckian Memetic Algorithms and Local Search [0.0]
  We propose a novel approach integrating a two-stage training technique with population-based optimization algorithms incorporating local search capabilities. Our experiments demonstrate that the proposed method outperforms state-of-the-art gradient-based techniques.
  arXiv  Detail & Related papers  (2024-10-26T17:31:15Z)
• A theoretical and empirical study of new adaptive algorithms with additional momentum steps and shifted updates for stochastic non-convex optimization [0.0]
  It is thought that adaptive optimization algorithms represent the key pillar behind the of the Learning field. In this paper we introduce adaptive momentum techniques for different non-smooth objective problems.
  arXiv  Detail & Related papers  (2021-10-16T09:47:57Z)
• A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems [9.653157073271021]
  We develop a decentralized adaptive momentum (ADAM)-type algorithm for solving min-max optimization problem. We obtain non-asymptotic rates of convergence of the proposed algorithm for finding a (stochastic) first-order Nash equilibrium point.
  arXiv  Detail & Related papers  (2021-06-10T22:32:01Z)
• Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimator [75.05106948314956]
  We show that an increasing large momentum parameter for the first-order moment is sufficient for adaptive scaling. We also give insights for increasing the momentum in a stagewise manner in accordance with stagewise decreasing step size.
  arXiv  Detail & Related papers  (2021-04-30T08:50:24Z)
• Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization [98.0595480384208]
  We propose a generalization extraient spaces which converges to a stationary point. The algorithm applies not only to general $p$-normed spaces, but also to general $p$-dimensional vector spaces.
  arXiv  Detail & Related papers  (2020-10-31T21:35:42Z)
• IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method [64.15649345392822]
  We introduce a framework for designing primal methods under the decentralized optimization setting where local functions are smooth and strongly convex. Our approach consists of approximately solving a sequence of sub-problems induced by the accelerated augmented Lagrangian method. When coupled with accelerated gradient descent, our framework yields a novel primal algorithm whose convergence rate is optimal and matched by recently derived lower bounds.
  arXiv  Detail & Related papers  (2020-06-11T18:49:06Z)
• Convergence of adaptive algorithms for weakly convex constrained optimization [59.36386973876765]
  We prove the $mathcaltilde O(t-1/4)$ rate of convergence for the norm of the gradient of Moreau envelope. Our analysis works with mini-batch size of $1$, constant first and second order moment parameters, and possibly smooth optimization domains.
  arXiv  Detail & Related papers  (2020-06-11T17:43:19Z)
• Adaptive First-and Zeroth-order Methods for Weakly Convex Stochastic Optimization Problems [12.010310883787911]
  We analyze a new family of adaptive subgradient methods for solving an important class of weakly convex (possibly nonsmooth) optimization problems. Experimental results indicate how the proposed algorithms empirically outperform its zerothorder gradient descent and its design variant.
  arXiv  Detail & Related papers  (2020-05-19T07:44:52Z)
• Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization [71.03797261151605]
  Adaptivity is an important yet under-studied property in modern optimization theory. Our algorithm is proved to achieve the best-available convergence for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.
  arXiv  Detail & Related papers  (2020-02-13T05:42:27Z)
• Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets [71.05306664267832]
  Adaptive algorithms perform gradient updates using the history of gradients and are ubiquitous in training deep neural networks. In this paper we analyze a variant of OptimisticOA algorithm for nonconcave minmax problems. Our experiments show that adaptive GAN non-adaptive gradient algorithms can be observed empirically.
  arXiv  Detail & Related papers  (2019-12-26T22:10:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.