Optimization Theory: A Concise Introduction
Abstract
Mathematically, most of the interesting optimization problems can be formulated to optimize some objective function, subject to some equality and/or inequality constraints. This book introduces some classical and basic results of optimization theory, including nonlinear programming with Lagrange multiplier method, the Karush-Kuhn-Tucker method, Fritz John’s method, problems with convex or quasi-convex constraints, and linear programming with geometric method and simplex method. A slim book such as this which touches on major aspects of optimization theory will be very much needed for most readers. We present nonlinear programming, convex programming, and linear programming in a self-contained manner. This book is for a one-semester course for upper level undergraduate students or first/second year graduate students. It should also be useful for researchers working on many interdisciplinary areas other than optimization.
Publication Date
1-1-2018
Publication Title
Optimization Theory: A Concise Introduction
Number of Pages
1-223
Document Type
Article; Book Chapter
Personal Identifier
scopus
DOI Link
https://doi.org/10.1142/10923
Copyright Status
Unknown
Socpus ID
85051615238 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85051615238
STARS Citation
Yong, Jiongmin, "Optimization Theory: A Concise Introduction" (2018). Scopus Export 2015-2019. 8816.
https://stars.library.ucf.edu/scopus2015/8816