Engineering Optimization: Theory and Practice, 4th Edition
Engineering Optimization: Theory and Practice, 4th Edition
ISBN: 9780470183526
Jun 2009
840 pages
Out of stock
$209.95
Description
Technology/Engineering/Mechanical
Helps you move from theory to optimizing engineering systems in almost any industry
Now in its Fourth Edition, Professor Singiresu Rao's acclaimed text Engineering Optimization enables readers to quickly master and apply all the important optimization methods in use today across a broad range of industries. Covering both the latest and classical optimization methods, the text starts off with the basics and then progressively builds to advanced principles and applications.
This comprehensive text covers nonlinear, linear, geometric, dynamic, and stochastic programming techniques as well as more specialized methods such as multiobjective, genetic algorithms, simulated annealing, neural networks, particle swarm optimization, ant colony optimization, and fuzzy optimization. Each method is presented in clear, straightforward language, making even the more sophisticated techniques easy to grasp. Moreover, the author provides:

Case examples that show how each method is applied to solve realworld problems across a variety of industries

Review questions and problems at the end of each chapter to engage readers in applying their newfound skills and knowledge

Examples that demonstrate the use of MATLAB® for the solution of different types of practical optimization problems

References and bibliography at the end of each chapter for exploring topics in greater depth

Answers to Review Questions available on the author's Web site to help readers to test their understanding of the basic concepts
With its emphasis on problemsolving and applications, Engineering Optimization is ideal for upperlevel undergraduates and graduate students in mechanical, civil, electrical, chemical, and aerospace engineering. In addition, the text helps practicing engineers in almost any industry design improved, more efficient systems at less cost.
Related Resources
Instructor
Preface xvii
1 Introduction to Optimization 1
1.1 Introduction 1
1.2 Historical Development 3
1.3 Engineering Applications of Optimization 5
1.4 Statement of an Optimization Problem 6
1.5 Classification of Optimization Problems 14
1.6 Optimization Techniques 35
1.7 Engineering Optimization Literature 35
1.8 Solution of Optimization Problems Using MATLAB 36
2 Classical Optimization Techniques 63
2.1 Introduction 63
2.2 SingleVariable Optimization 63
2.3 Multivariable Optimization with No Constraints 68
2.4 Multivariable Optimization with Equality Constraints 75
2.5 Multivariable Optimization with Inequality Constraints 93
2.6 Convex Programming Problem 104
3 Linear Programming I: Simplex Method 119
3.1 Introduction 119
3.2 Applications of Linear Programming 120
3.3 Standard Form of a Linear Programming Problem 122
3.4 Geometry of Linear Programming Problems 124
3.5 Definitions and Theorems 127
3.6 Solution of a System of Linear Simultaneous Equations 133
3.7 Pivotal Reduction of a General System of Equations 135
3.8 Motivation of the Simplex Method 138
3.9 Simplex Algorithm 139
3.10 Two Phases of the Simplex Method 150
3.11 MATLAB Solution of LP Problems 156
4 Linear Programming II: Additional Topics and Extensions 177
4.1 Introduction 177
4.2 Revised Simplex Method 177
4.3 Duality in Linear Programming 192
4.4 Decomposition Principle 200
4.5 Sensitivity or Postoptimality Analysis 207
4.6 Transportation Problem 220
4.7 Karmarkar's Interior Method 222
4.8 Quadratic Programming 229
4.9 MATLAB Solutions 235
5 Nonlinear Programming I: OneDimensional Minimization Methods 248
5.1 Introduction 248
5.2 Unimodal Function 253
ELIMINATION METHODS 254
5.3 Unrestricted Search 254
5.4 Exhaustive Search 256
5.5 Dichotomous Search 257
5.6 Interval Halving Method 260
5.7 Fibonacci Method 263
5.8 Golden Section Method 267
5.9 Comparison of Elimination Methods 271
INTERPOLATION METHODS 271
5.10 Quadratic Interpolation Method 273
5.11 Cubic Interpolation Method 280
5.12 Direct Root Methods 286
5.13 Practical Considerations 293
5.14 MATLAB Solution of OneDimensional Minimization Problems 294
6 Nonlinear Programming II: Unconstrained Optimization Techniques 301
6.1 Introduction 301
DIRECT SEARCH METHODS 309
6.2 Random Search Methods 309
6.3 Grid Search Method 314
6.4 Univariate Method 315
6.5 Pattern Directions 318
6.6 Powell's Method 319
6.7 Simplex Method 328
INDIRECT SEARCH (DESCENT) METHODS 335
6.8 Gradient of a Function 335
6.9 Steepest Descent (Cauchy) Method 339
6.10 Conjugate Gradient (Fletcher–Reeves) Method 341
6.11 Newton's Method 345
6.12 Marquardt Method 348
6.13 QuasiNewton Methods 350
6.14 Davidon–Fletcher–Powell Method 354
6.15 Broyden–Fletcher–Goldfarb–Shanno Method 360
6.16 Test Functions 363
6.17 MATLAB Solution of Unconstrained Optimization Problems 365
7 Nonlinear Programming III: Constrained Optimization Techniques 380
7.1 Introduction 380
7.2 Characteristics of a Constrained Problem 380
DIRECT METHODS 383
7.3 Random Search Methods 383
7.4 Complex Method 384
7.5 Sequential Linear Programming 387
7.6 Basic Approach in the Methods of Feasible Directions 393
7.7 Zoutendijk's Method of Feasible Directions 394
7.8 Rosen's Gradient Projection Method 404
7.9 Generalized Reduced Gradient Method 412
7.10 Sequential Quadratic Programming 422
INDIRECT METHODS 428
7.11 Transformation Techniques 428
7.12 Basic Approach of the Penalty Function Method 430
7.13 Interior Penalty Function Method 432
7.14 Convex Programming Problem 442
7.15 Exterior Penalty Function Method 443
7.16 Extrapolation Techniques in the Interior Penalty Function Method 447
7.17 Extended Interior Penalty Function Methods 451
7.18 Penalty Function Method for Problems with Mixed Equality and Inequality Constraints 453
7.19 Penalty Function Method for Parametric Constraints 456
7.20 Augmented Lagrange Multiplier Method 459
7.21 Checking the Convergence of Constrained Optimization Problems 464
7.22 Test Problems 467
7.23 MATLAB Solution of Constrained Optimization Problems 474
8 Geometric Programming 492
8.1 Introduction 492
8.2 Posynomial 492
8.3 Unconstrained Minimization Problem 493
8.4 Solution of an Unconstrained Geometric Programming Program Using Differential Calculus 493
8.5 Solution of an Unconstrained Geometric Programming Problem Using Arithmetic–Geometric Inequality 500
8.6 Primal–Dual Relationship and Sufficiency Conditions in the Unconstrained Case 501
8.7 Constrained Minimization 508
8.8 Solution of a Constrained Geometric Programming Problem 509
8.9 Primal and Dual Programs in the Case of LessThan Inequalities 510
8.10 Geometric Programming with Mixed Inequality Constraints 518
8.11 Complementary Geometric Programming 520
8.12 Applications of Geometric Programming 525
9 Dynamic Programming 544
9.1 Introduction 544
9.2 Multistage Decision Processes 545
9.3 Concept of Suboptimization and Principle of Optimality 549
9.4 Computational Procedure in Dynamic Programming 553
9.5 Example Illustrating the Calculus Method of Solution 555
9.6 Example Illustrating the Tabular Method of Solution 560
9.7 Conversion of a Final Value Problem into an Initial Value Problem 566
9.8 Linear Programming as a Case of Dynamic Programming 569
9.9 Continuous Dynamic Programming 573
9.10 Additional Applications 576
10 Integer Programming 588
10.1 Introduction 588
INTEGER LINEAR PROGRAMMING 589
10.2 Graphical Representation 589
10.3 Gomory's Cutting Plane Method 591
10.4 Balas' Algorithm for Zero–One Programming Problems 604
INTEGER NONLINEAR PROGRAMMING 606
10.5 Integer Polynomial Programming 606
10.6 BranchandBound Method 609
10.7 Sequential Linear Discrete Programming 614
10.8 Generalized Penalty Function Method 619
10.9 Solution of Binary Programming Problems Using MATLAB 624
11 Stochastic Programming 632
11.1 Introduction 632
11.2 Basic Concepts of Probability Theory 632
11.3 Stochastic Linear Programming 647
11.4 Stochastic Nonlinear Programming 652
11.5 Stochastic Geometric Programming 659
12 Optimal Control and Optimality Criteria Methods 668
12.1 Introduction 668
12.2 Calculus of Variations 668
12.3 Optimal Control Theory 678
12.4 Optimality Criteria Methods 683
13 Modern Methods of Optimization 693
13.1 Introduction 693
13.2 Genetic Algorithms 694
13.3 Simulated Annealing 702
13.4 Particle Swarm Optimization 708
13.5 Ant Colony Optimization 714
13.6 Optimization of Fuzzy Systems 722
13.7 NeuralNetworkBased Optimization 727
14 Practical Aspects of Optimization 737
14.1 Introduction 737
14.2 Reduction of Size of an Optimization Problem 737
14.3 Fast Reanalysis Techniques 740
14.4 Derivatives of Static Displacements and Stresses 745
14.5 Derivatives of Eigenvalues and Eigenvectors 747
14.6 Derivatives of Transient Response 749
14.7 Sensitivity of Optimum Solution to Problem Parameters 751
14.8 Multilevel Optimization 755
14.9 Parallel Processing 760
14.10 Multiobjective Optimization 761
14.11 Solution of Multiobjective Problems Using MATLAB 767
A Convex and Concave Functions 779
B Some Computational Aspects of Optimization 784
B.1 Choice of Method 784
B.2 Comparison of Unconstrained Methods 784
B.3 Comparison of Constrained Methods 785
B.4 Availability of Computer Programs 786
B.5 Scaling of Design Variables and Constraints 787
B.6 Computer Programs for Modern Methods of Optimization 788
C Introduction to MATLAB 791
C.1 Features and Special Characters 791
C.2 Defining Matrices in MATLAB 792
C.3 CREATING mFILES 793
C.4 Optimization Toolbox 793
Answers to Selected Problems 795
Index 803