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A First Course in Optimization

By: (Author) Charles Byrne , (Author) Charles Byrne

Manufacture on Demand

Ksh 18,000.00

Format: Hardback or Cased Book

ISBN-10: 1482226561

ISBN-13: 9781482226560

Publisher: Taylor & Francis Inc

Imprint: Chapman & Hall/CRC

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Aug 11th, 2014

Print length: 316 Pages

Weight: 576 grams

Dimensions (height x width x thickness): 15.50 x 24.30 x 2.00 cms

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This text is designed for a one-semester course in optimization taken by advanced undergraduate and beginning graduate students in the mathematical sciences and engineering. It teaches students the basics of continuous optimization and helps them better understand the mathematics from previous courses. The book focuses on general problems and the underlying theory. It covers the fundamental problems of constrained and unconstrained optimization, linear and convex programming, basic iterative solution algorithms, and more general iterative optimization methods.

Give Your Students the Proper Groundwork for Future Studies in Optimization

A First Course in Optimization is designed for a one-semester course in optimization taken by advanced undergraduate and beginning graduate students in the mathematical sciences and engineering. It teaches students the basics of continuous optimization and helps them better understand the mathematics from previous courses.

The book focuses on general problems and the underlying theory. It introduces all the necessary mathematical tools and results. The text covers the fundamental problems of constrained and unconstrained optimization as well as linear and convex programming. It also presents basic iterative solution algorithms (such as gradient methods and the Newton–Raphson algorithm and its variants) and more general iterative optimization methods.

This text builds the foundation to understand continuous optimization. It prepares students to study advanced topics found in the author’s companion book, Iterative Optimization in Inverse Problems, including sequential unconstrained iterative optimization methods.


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