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文章基本信息

  • 标题:Potential-Function Proofs for Gradient Methods
  • 本地全文:下载
  • 作者:Nikhil Bansal ; Anupam Gupta
  • 期刊名称:Theory of Computing
  • 印刷版ISSN:1557-2862
  • 电子版ISSN:1557-2862
  • 出版年度:2019
  • 卷号:15
  • 页码:1-32
  • DOI:10.4086/toc.2019.v015a004
  • 出版社:University of Chicago
  • 摘要:This note discusses proofs of convergence for gradient methods (also called “first-order methods”) based on simple potential-function arguments. We cover methods like gradient descent (for both smooth and non-smooth settings), mirror descent, and some accelerated variants. We hope the structure and presentation of these amortized-analysis proofs will be useful as a guiding principle in learning and using these proofs.
  • 关键词:convex optimization; potential functions; amortized analysis
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