Convexity and Optimization in R-n 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Convexity and Optimization in R-n

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Berkovitz, Leonard D. 作者
Wiley Publisher
译者
2001-12 出版日期
280 页数
1143.00 元 价格
Hard Cover
丛书系列
9780471352815 图书编码

Convexity and Optimization in R-n 在线电子书 图书标签: 数学  优化  R  Academic   


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发表于2024-10-01


Convexity and Optimization in R-n 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Convexity and Optimization in R-n 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Convexity and Optimization in R-n 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



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Convexity and Optimization in R-n 在线电子书 著者简介

LEONARD D. BERKOVITZ, PhD, is Professor of Mathematics at Purdue University. He previously worked at the RAND Corporation and has served on the editorial boards of several journals, including terms as Managing Editor of the SIAM Journal on Control and as a member of the Editorial Committee of Mathematical Reviews.


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Convexity and Optimization in R-n 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Convexity and Optimization in R-n 在线电子书 图书描述

A comprehensive introduction to convexity and optimization in Rn This book presents the mathematics of finite dimensional constrained optimization problems. It provides a basis for the further mathematical study of convexity, of more general optimization problems, and of numerical algorithms for the solution of finite dimensional optimization problems. For readers who do not have the requisite background in real analysis, the author provides a chapter covering this material. The text features abundant exercises and problems designed to lead the reader to a fundamental understanding of the material. Convexity and Optimization in Rn provides detailed discussion of: Requisite topics in real analysis Convex sets Convex functions Optimization problems Convex programming and duality The simplex method A detailed bibliography is included for further study and an index offers quick reference. Suitable as a text for both graduate and undergraduate students in mathematics and engineering, this accessible text is written from extensively class-tested notes.

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