Practical Mathematical Optimization : Basic Optimization Theory and Gradient-Based Algorithms / by Jan A Snyman, Daniel N Wilke.
Snyman, Jan A.; Wilke, Daniel N. author.
2018
QA402.5
Available Online

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Title Practical Mathematical Optimization : Basic Optimization Theory and Gradient-Based Algorithms / by Jan A Snyman, Daniel N Wilke.
Author Snyman, Jan A.
Edition 2nd ed. 2018.
ISBN 9783319775869
3319775863
9783319775852
3319775855
DOI https://doi.org/10.1007/978-3-319-77586-9
Published Cham : Springer International Publishing : Imprint: Springer, 2018.
Language English
Description 1 online resource (xxvi, 372 pages) : illustrations.
Item Number 10.1007/978-3-319-77586-9 doi
Call Number QA402.5
Dewey Decimal Classification 519.6
Summary This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences. Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and directly applicable. Numerical examples and exercises are included to encourage senior- to graduate-level students to plan, execute, and reflect on numerical investigations. By gaining a deep understanding of the conceptual material presented, students, scientists, and engineers will be able to develop systematic and scientific numerical investigative skills.-- Provided by publisher.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Added Author Wilke, Daniel N. author.
Series Springer optimization and its applications ; 133.
Available in Other Form Print version: 9783319775852
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1.Introduction
2.Line search descent methods for unconstrained minimization.-3. Standard methods for constrained optimization.-4. Basic Example Problems
5. Some Basic Optimization Theorems
6. New gradient-based trajectory and approximation methods
7. Surrogate Models
8. Gradient-only solution strategies
9. Practical computational optimization using Python
Appendix
Index.

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