Get Free Shipping on orders over $79
Optimization Theory : A Concise Introduction - Jiongmin Yong

Optimization Theory

A Concise Introduction

By: Jiongmin Yong

eText | 16 May 2018

At a Glance

eText


$32.99

or 4 interest-free payments of $8.25 with

 or 

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

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.

Contents:
  • Mathematical Preparation (including Basics of Euclidean Space, Linear Algebra, Limits, Continuity, and Differentiability of Functions)
  • Optimization Problems and Existence of Optimal Solutions
  • Necessary and Sufficient Conditions of Optimal Solutions (including Problems with No Constraint, with Equality Constraints, and with Equality and Inequality Constraints)
  • Problems with Convexity and Quasi-Convexity Conditions (including Convex Sets and Convex Functions, Optimization Problems with Convex and Quasi-Convex Constraints, Lagrange Duality)
  • Linear Programming (including Geometric Method, Simplex Method, Sensitivity Analysis, and Duality Theory)

Readership: Undergraduates; graduates and researchers interested in classical and basic optimization theory.
Keywords:Optimization;Nonlinear Programming;Linear ProgrammingReview:Key Features:
  • Based on the preparation material (standard calculus and linear algebra presented in the first chapter), the presentation of all the major results of optimization are self-contained
  • We use Ekeland's variational principle to prove Fritz John's optimality conditions. As far as the author's knowledge, this is new
  • The theoretic results and examples have been balanced. All the major theorems are companioned by its proof and some examples. This enables the readers who are not interested in the proofs to proceed to learn how to use the theorems from the examples
on
Desktop
Tablet
Mobile

More in Optimisation

AI Breaking Boundaries - Avinash Vanam

eBOOK