Cargando…
Engineering optimization : applications, methods and analysis /
Optimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. Engineering Optimization provides a practically-focused in...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons,
2018.
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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;#x93;Max, Maxâ#x80;#x93;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