A First Course in Optimization  book cover
1st Edition

A First Course in Optimization

ISBN 9781482226560
Published August 11, 2014 by Chapman and Hall/CRC
316 Pages

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Book Description

Give Your Students the Proper Groundwork for Future Studies in Optimization

A First Course in Optimization is designed for a one-semester course in optimization taken by advanced undergraduate and beginning graduate students in the mathematical sciences and engineering. It teaches students the basics of continuous optimization and helps them better understand the mathematics from previous courses.

The book focuses on general problems and the underlying theory. It introduces all the necessary mathematical tools and results. The text covers the fundamental problems of constrained and unconstrained optimization as well as linear and convex programming. It also presents basic iterative solution algorithms (such as gradient methods and the Newton–Raphson algorithm and its variants) and more general iterative optimization methods.

This text builds the foundation to understand continuous optimization. It prepares students to study advanced topics found in the author’s companion book, Iterative Optimization in Inverse Problems, including sequential unconstrained iterative optimization methods.

Table of Contents

Optimization without Calculus
Chapter Summary
The Arithmetic Mean-Geometric Mean Inequality
An Application of the AGM Inequality: the Number e
Extending the AGM Inequality
Optimization Using the AGM Inequality
The Holder and Minkowski Inequalities
Cauchy's Inequality
Optimizing using Cauchy's Inequality
An Inner Product for Square Matrices
Discrete Allocation Problems

Geometric Programming
Chapter Summary
An Example of a GP Problem
Posynomials and the GP Problem
The Dual GP Problem
Solving the GP Problem
Solving the DGP Problem
Constrained Geometric Programming

Basic Analysis
Chapter Summary
Minima and Infima
Limsup and Liminf
Another View

Convex Sets
Chapter Summary
The Geometry of Real Euclidean Space
A Bit of Topology
Convex Sets in RJ
More on Projections
Linear and Affine Operators on RJ
The Fundamental Theorems
Block-Matrix Notation
Theorems of the Alternative
Another Proof of Farkas' Lemma
Gordan's Theorem Revisited

Vector Spaces and Matrices
Chapter Summary
Vector Spaces
Basic Linear Algebra
LU and QR Factorization
The LU Factorization

Linear Programming
Chapter Summary
Primal and Dual
Converting a Problem to PS Form
Duality Theorems
The Basic Strong Duality Theorem
Another Proof
Proof of Gale's Strong Duality Theorem
Some Examples
The Simplex Method
Yet Another Proof
The Sherman–Morrison–Woodbury Identity
An Example of the Simplex Method
Another Example
Some Possible Difficulties
Topics for Projects

Matrix Games and Optimization
Chapter Summary
Two-Person Zero-Sum Games
Deterministic Solutions
Randomized Solutions
Symmetric Games
Positive Games
Example: The "Bluffing" Game
Learning the Game
Non-Constant-Sum Games

Chapter Summary
Directional Derivative
Partial Derivatives
Some Examples
Gâteaux Derivative
Fréchet Derivative
The Chain Rule

Convex Functions
Chapter Summary
Functions of a Single Real Variable
Functions of Several Real Variables
Sub-Differentials and Sub-Gradients
Sub-Gradients and Directional Derivatives
Functions and Operators
Convex Sets and Convex Functions

Convex Programming
Chapter Summary
The Primal Problem
From Constrained to Unconstrained
Saddle Points
The Karush–Kuhn–Tucker Theorem
On Existence of Lagrange Multipliers
The Problem of Equality Constraints
Two Examples
The Dual Problem
Nonnegative Least-Squares Solutions
An Example in Image Reconstruction
Solving the Dual Problem
Minimum One-Norm Solutions

Iterative Optimization
Chapter Summary
The Need for Iterative Methods
Optimizing Functions of a Single Real Variable
The Newton–Raphson Approach
Approximate Newton–Raphson Methods
Derivative-Free Methods
Rates of Convergence
Descent Methods
Optimizing Functions of Several Real Variables
Auxiliary-Function Methods
Projected Gradient-Descent Methods
Feasible-Point Methods
Quadratic Programming
Simulated Annealing

Solving Systems of Linear Equations
Chapter Summary
Arbitrary Systems of Linear Equations
Nonnegative Systems of Linear Equations
Regularized SMART and EMML
Block-Iterative Methods

Conjugate-Direction Methods
Chapter Summary
Iterative Minimization
Quadratic Optimization
Conjugate Bases for RJ
The Conjugate Gradient Method
Krylov Subspaces
Extensions of the CGM

Chapter Summary
Contraction Operators
Orthogonal-Projection Operators
Two Useful Identities
Averaged Operators
Gradient Operators
Affine-Linear Operators
Paracontractive Operators
Matrix Norms

Looking Ahead
Chapter Summary
Sequential Unconstrained Minimization
Examples of SUM
Auxiliary-Function Methods
The SUMMA Class of AF Methods



Exercises appear at the end of each chapter.

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"This book, split in fifteen chapters, presents a thorough introduction to optimization for undergraduate and graduate courses in science and engineering and forms a useful toolkit of optimization techniques across several domains. … Throughout this very interesting book, a number of solved and unsolved exercises provide the reader with all the necessary tools to understand the techniques and their applicability in real-life problems."
Zentralblatt MATH 1312