Convex Optimization: Algorithms and Complexity 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

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Sébastien Bubeck 作者

Now Publishers Inc

譯者

2015 出版日期

142 頁數

0 價格

Foundations and Trends® in Machine Learning 叢書系列

9781601988607 圖書編碼

Convex Optimization: Algorithms and Complexity 在線電子書圖書標籤: 凸優化 Optimization Machine_Learning 數學

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發表於2024-11-24

Convex Optimization: Algorithms and Complexity 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Convex Optimization: Algorithms and Complexity 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Convex Optimization: Algorithms and Complexity 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

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Convex Optimization: Algorithms and Complexity 在線電子書著者簡介

Convex Optimization: Algorithms and Complexity 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Convex Optimization: Algorithms and Complexity 在線電子書圖書描述

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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