Convex Optimization is popular PDF and ePub book, written by Sébastien Bubeck in 2015-11-12, it is a fantastic choice for those who relish reading online the Convex domains genre. Let's immerse ourselves in this engaging Convex domains book by exploring the summary and details provided below. Remember, Convex Optimization can be Read Online from any device for your convenience.
Convex Optimization Book PDF Summary
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.
Detail Book of Convex Optimization PDF
- Author : Sébastien Bubeck
- Release : 12 November 2015
- Publisher : Foundations and Trends (R) in Machine Learning
- ISBN : 1601988605
- Genre : Convex domains
- Total Page : 142 pages
- Language : English
- PDF File Size : 8,6 Mb
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