Engineering optimization: applications, methods and analysis

✍ Scribed by Rhinehart, R. Russell

• Publisher: John Wiley & Sons
• Year: 2018
• Tongue: English
• Leaves: 772
• Edition: First edition
• Category: Library

✦ Table of Contents

Content: Intro
Title Page
Copyright Page
Contents
Preface
Acknowledgments
Nomenclature
About the Companion Website
Section 1 Introductory Concepts
Chapter 1 Optimization: Introduction and Concepts
1.1 Optimization and Terminology
1.2 Optimization Concepts and Definitions
1.3 Examples
1.4 Terminology Continued
1.4.1 Constraint
1.4.2 Feasible Solutions
1.4.3 Minimize or Maximize
1.4.4 Canonical Form of the Optimization Statement
1.5 Optimization Procedure
1.6 Issues That Shape Optimization Procedures
1.7 Opposing Trends
1.8 Uncertainty 1.9 Over- and Under-specification in Linear Equations1.10 Over- and Under-specification in Optimization
1.11 Test Functions
1.12 Significant Dates in Optimization
1.13 Iterative Procedures
1.14 Takeaway
1.15 Exercises
Chapter 2 Optimization Application Diversity and Complexity
2.1 Optimization
2.2 Nonlinearity
2.3 Min, Max, Minâ#x80
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Min,
2.4 Integers and Other Discretization
2.5 Conditionals and Discontinuities: Cliffs Ridges/Valleys
2.6 Procedures, Not Equations
2.7 Static and Dynamic Models
2.8 Path Integrals 2.9 Economic Optimization and Other Nonadditive Cost Functions2.10 Reliability
2.11 Regression
2.12 Deterministic and Stochastic
2.13 Experimental w.r.t. Modeled OF
2.14 Single and Multiple Optima
2.15 Saddle Points
2.16 Inflections
2.17 Continuum and Discontinuous DVs
2.18 Continuum and Discontinuous Models
2.19 Constraints and Penalty Functions
2.20 Ranks and Categorization: Discontinuous OFs
2.21 Underspecified OFs
2.22 Takeaway
2.23 Exercises
Chapter 3 Validation: Knowing That the Answer Is Right
3.1 Introduction
3.2 Validation
3.3 Advice on Becoming Proficient 3.4 Takeaway3.5 Exercises
Section 2 Univariate Search Techniques
Chapter 4 Univariate (Single DV) Search Techniques
4.1 Univariate (Single DV)
4.2 Analytical Method of Optimization
4.2.1 Issues with the Analytical Approach
4.3 Numerical Iterative Procedures
4.3.1 NewtonÂś Methods
4.3.2 Successive Quadratic (A Surrogate Model or Approximating Model Method)
4.4 Direct Search Approaches
4.4.1 Bisection Method
4.4.2 Golden Section Method
4.4.3 Perspective at This Point
4.4.4 Heuristic Direct Search
4.4.5 Leapfrogging
4.4.6 LF for Stochastic Functions 4.5 Perspectives on Univariate Search Methods4.6 Evaluating Optimizers
4.7 Summary of Techniques
4.7.1 Analytical Method
4.7.2 NewtonÂś (and Variants Like Secant)
4.7.3 Successive Quadratic
4.7.4 Golden Section Method
4.7.5 Heuristic Direct
4.7.6 Leapfrogging
4.8 Takeaway
4.9 Exercises
Chapter 5 Path Analysis
5.1 Introduction
5.2 Path Examples
5.3 Perspective About Variables
5.4 Path Distance Integral
5.5 Accumulation along a Path
5.6 Slope along a Path
5.7 Parametric Path Notation
5.8 Takeaway
5.9 Exercises
Chapter 6 Stopping and Convergence Criteria: 1-D Applications

✦ Subjects

Engineering -- Mathematical models.;Mathematical optimization.;TECHNOLOGY & ENGINEERING -- Engineering (General);TECHNOLOGY & ENGINEERING -- Reference.