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Algorithms for Optimization, second edition
List Price:
$115.00
| Expected release date is Jan 5th 2027 |
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Product Details
Author:
Mykel J. Kochenderfer, Tim A. Wheeler
Format:
Hardcover
Pages:
634
Publisher:
MIT Press (January 5, 2027)
Imprint:
The MIT Press
Release Date:
January 5, 2027
Language:
English
Audience:
General/trade
ISBN-13:
9780262058162
ISBN-10:
0262058162
Weight:
20oz
Dimensions:
8" x 9"
File:
RandomHouse-PRH_Book_Company_PRH_PRT_Onix_delta_active_D20260831T223942_157678713-20260831.xml
Folder:
RandomHouse
List Price:
$115.00
Country of Origin:
United States
Pub Discount:
65
Case Pack:
12
As low as:
$88.55
Publisher Identifier:
P-RH
Discount Code:
A
QuickShip:
Yes
Overview
A comprehensive, practical introduction to optimization updated with new chapters on duality, quadratic programming, and disciplined convex programming.
The extensively updated second edition of this popular textbook provides a comprehensive introduction to optimization with a focus on practical algorithms. Mykel Kochenderfer and Tim Wheeler approach optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. Suitable for advanced undergraduates and graduate students in mathematics, statistics, computer science, engineering, and operations research and as a reference for professionals, the text provides concrete implementations in the Julia programming language.
Second edition highlights:
The extensively updated second edition of this popular textbook provides a comprehensive introduction to optimization with a focus on practical algorithms. Mykel Kochenderfer and Tim Wheeler approach optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. Suitable for advanced undergraduates and graduate students in mathematics, statistics, computer science, engineering, and operations research and as a reference for professionals, the text provides concrete implementations in the Julia programming language.
Second edition highlights:
- New chapters on duality, quadratic programming, and disciplined convex programming
- Additional methods covered, from new ways to estimate gradients to multifidelity techniques
- Improved accessibility, expanded explanations, updated references, and streamlined algorithms
- More exercises, example applications, and figures
