Convex Optimization: Algorithms and Complexity 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Sébastien Bubeck 作者

Now Publishers Inc

译者

2015 出版日期

142 页数

0 价格

Foundations and Trends® in Machine Learning 丛书系列

9781601988607 图书编码

Convex Optimization: Algorithms and Complexity 在线电子书图书标签: 凸优化 Optimization Machine_Learning 数学

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Convex Optimization: Algorithms and Complexity 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

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Convex Optimization: Algorithms and Complexity 在线电子书著者简介

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Convex Optimization: Algorithms and Complexity 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Convex Optimization: Algorithms and Complexity 在线电子书图书描述

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 black-box optimization, strongly influenced by the seminal book by Nesterov, includes the analysis of cutting plane methods, as well as (accelerated) gradient descent schemes. Special attention is also given to non-Euclidean settings (relevant algorithms include Frank-Wolfe, mirror descent, and dual averaging), and discussing their relevance in machine learning. The text provides a gentle introduction to structural optimization with FISTA (to optimize a sum of a smooth and a simple non-smooth term), saddle-point mirror prox (Nemirovski's alternative to Nesterov's smoothing), and a concise description of interior point methods. In stochastic optimization it discusses stochastic gradient descent, mini-batches, random coordinate descent, and sublinear algorithms. It also briefly touches upon convex relaxation of combinatorial problems and the use of randomness to round solutions, as well as random walks based methods.

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