This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. It begins with the fundamental theory of black-box optimization and proceeds to guide the reader through recent advances in structural optimization and stochastic optimization. The presentation of ...
Convex Optimization Algorithms and Complexity 下载积分: 900 内容提示: Foundations and Trends R?in Machine LearningVol. 8, No. 3-4 (2015) 231–358c ? 2015 S. BubeckDOI: 10.1561/2200000050Convex Optimization: Algorithms andComplexitySébastien BubeckTheory Group, Microsoft Researchsebubeck@microsoft....
optimization, strongly influenced by Nesterov’s seminal book and Nemirovski’s lecture notes, includes the analysis of cutting plane methods, as well as (accelerated) gradient descent schemes. We also pay special attention to non-Euclidean settings (relevant algorithms include Frank-Wolfe, mirror ...
Complexity and algorithms for convex network optimization and other nonlinear problems. 4OR, 3(3):171-216, 2005.Hochbaum, D. S. (2005) Complexity and algorithms for convex network optimization and other nonlinear problems. 4OR 3: pp. 171-216...
2017Convex optimization_ Algorithms and complexity阅读笔记 1 介绍一些概念 本专题的总体目标是介绍凸优化中的主要复杂性定理和相应的算法。我们将重点放在凸优化的五个主要结果上,这些结果给出了本文的整体结构:存在具有最优预言复杂度的有效切面方法(第2章),对一阶预言复杂度和曲率之间关系的完整表征。目标函数(第...
This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. Starting from the fundamental theory of black-box optimization, the material progresses towards recent advances in structural optimization and stochastic optimization. Our presentation of black-box op...
2017Convex optimization_ Algorithms and complexity阅读笔记 1 介绍一些概念 本专题的总体目标是介绍凸优化中的主要复杂性定理和相应的算法。我们将重点放在凸优化的五个主要结果上,这些结果给出了本文的整体结构:存在具有最优预言复杂度的有效切面方法(第2章),对一阶预言复杂度和曲率之间关系的完整表征。目标函数(第...
Convex Optimization: Algorithms and Complexity 电子书 读后感 评分☆☆☆ 评分☆☆☆ 评分☆☆☆ 评分☆☆☆ 评分☆☆☆ 类似图书 点击查看全场最低价 出版者:Now Publishers Inc 作者:Sébastien Bubeck 出品人: 页数:142 译者: 出版时间:2015 价格:0 ...
Particularly, given the inexact initialization oracle, our regularization-based algorithms achieve the best of both worlds - optimal reproducibility and near-optimal gradient complexity - for minimization and minimax optimization. With the inexact gradient oracle, the near-optimal guarantees also hold for ...
solving convex optimization problems • no analytical solution • reliable and efficient algorithms • computation time (roughly) proportional to max{n 3 , n 2 m, F}, where F is cost of evaluating f i ’s and their first and second derivatives • almost a technology using convex...