Nonlinear Programming
85% of respondents would recommend this to a friend
HKD 909
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A comprehensive and accessible presentation of algorithms for solving continuous optimization problems with extensive coverage of iterative optimization methods and a detailed treatment of interior point methods.
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產品詳情
- The third edition of the book is a thoroughly rewritten version of the 1999 2nd edition. New material was included, some of the old material was discarded, and a large portion of the remainder was reorganized or revised.This book provides a comprehensive and accessible presentation of algorithms for solving continuous optimization problems. It relies on rigorous mathematical analysis, but also aims at an intuitive exposition that makes use of visualization where possible. It places particular emphasis on modern developments, and their widespread applications in fields such as large-scale resource allocation problems, signal processing, and machine learning.The book was developed through instruction at MIT, focuses on nonlinear and other types of optimization: iterative algorithms for constrained and unconstrained optimization, Lagrange multipliers and duality, large scale problems, and the interface between continuous and discrete optimization. Among its special features, the book: 1) provides extensive coverage of iterative optimization methods within a unifying framework 2) provides a detailed treatment of interior point methods for linear programming 3) covers in depth duality theory from both a variational and a geometrical/convex analysis point of view 4) includes much new material on a number of topics, such as neural network training, large-scale optimization, signal processing, machine learning, and optimal control 5) includes a large number of examples and exercises detailed solutions of many of which are posted on the internet. Much supplementary/support material can be found at the publisher's and the author's web sites
| Publisher | Athena Scientific |
| Publication date | June 27, 2016 |
| Edition | 3rd |
| Language | English |
| Print length | 861 pages |
| ISBN-10 | 1886529051 |
| ISBN-13 | 978-1886529052 |
| Item Weight | 3.53 ounces (100.08 grams) |
| Dimensions | 6.5 x 1.75 x 9.5 inches (16.5 x 4.4 x 24.1 cm) |
Who Should Buy?
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Students in Academia
Ideal for students seeking a foundational understanding of nonlinear programming concepts and techniques in academic settings.
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Practicing Engineers
Useful for engineers who need practical applications of nonlinear programming in optimization problems within various industries.
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Research Analysts
Suitable for analysts involved in advanced research requiring methodologies for solving complex optimization challenges.
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Beginners in Math
Not suitable for those without a strong mathematical background, as concepts might be too complex to grasp without prior knowledge.
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Mathematics Editorial Review
The book "Nonlinear Programming" by Dimitri Bertsekas is highly recommended for anyone learning optimization theory. It covers useful theory and proposition, includes plenty of examples and meaningful exercises that help readers understand the concept. The appendix is also helpful for PhD research proofs. Though it is a mathematical book, readers must spend a lot of time with it to understand it better. Some reviewers mentioned that it is the book used to teach "Nonlinear Programming" at MIT. However, some readers new to the subject and not experienced in math may find it too strict and challenging to read. The book is nearly 800 pages of pure math, but it is excellent to own and understand concepts from.
Customer Reviews & Ratings
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5 星
86%
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4 星
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3 星
6%
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2 星
4%
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1 星
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優點
- Useful theory and proposition
- Plenty of examples and meaningful exercises
- Great for anyone learning optimization theory
- Excellent reference
- Helpful for PhD research proofs
- Famous and widely used
- Interesting and useful for machine learning
缺點
- Strict and challenging to read for those not experienced in math
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HKD 909
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特色和優勢
- Nonlinear Programming: 3rd Edition is a rewritten version of the 1999 2nd edition.
- The book provides a comprehensive and accessible presentation of algorithms for solving continuous optimization problems.
- It covers iterative algorithms, interior point methods, and duality theory.
- The book includes new materials on large-scale optimization, signal processing, machine learning, and optimal control.
- There are numerous examples and exercises with detailed solutions available online.
- The book was developed through instruction at MIT.
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