Applied Optimization
Formulation and Algorithms for Engineering Systems
$83.99 (P)
- Author: Ross Baldick, University of Texas, Austin
- Date Published: January 2009
- availability: Available
- format: Paperback
- isbn: 9780521100281
$
83.99
(P)
Paperback
Other available formats:
eBook
Looking for an examination copy?
If you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact collegesales@cambridge.org providing details of the course you are teaching.
-
The starting point in the formulation of any numerical problem is to take an intuitive idea about the problem in question and to translate it into precise mathematical language. This book provides step-by-step descriptions of how to formulate numerical problems so that they can be solved by existing software. It examines various types of numerical problems and develops techniques for solving them. A number of engineering case studies are used to illustrate in detail the formulation process. The case studies motivate the development of efficient algorithms that involve, in some cases, transformation of the problem from its initial formulation into a more tractable form.
Customer reviews
Not yet reviewed
Be the first to review
Review was not posted due to profanity
×Product details
- Date Published: January 2009
- format: Paperback
- isbn: 9780521100281
- length: 792 pages
- dimensions: 244 x 170 x 40 mm
- weight: 1.24kg
- contains: 180 b/w illus. 369 exercises
- availability: Available
Table of Contents
List of illustrations
Preface
1. Introduction
2. Problems, algorithms and solutions
3. Transformation of problems
Part I: Linear simultaneous equations
4. Case studies
5. Algorithms
Part II: Non-linear simultaneous equations
6. Case Studies
7. Algorithms
8. Solution of the case studies
Part III: Unconstrained optimization
9. Case studies
10 Algorithms
11. Solution of the case studies
Part IV: Equality-constrained optimization
12. Case studies
13. Algorithms for linear constraints
14. Algorithms for non-linear constraints
Part V: Inequality-constrained optimization
15. Case studies
16. Algorithms for non-negativity constraints
17. Algorithms for linear constraints
18. Solution of the linearly constrained case studies
19. Algorithms for non-linear constraints
20. Solution of the non-linearly constrained case studies
References
Index
Appendices.-
Resources for
Applied Optimization
Ross Baldick
Find resources associated with this title
Type Name Unlocked * Format Size Showing of
This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to instructors whose faculty status has been verified. To gain access to locked resources, instructors should sign in to or register for a Cambridge user account.
Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other instructors may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.
Supplementary resources are subject to copyright. Instructors are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.
If you are having problems accessing these resources please contact lecturers@cambridge.org.
Related Books
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org
Register Sign in» Proceed