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Optimization Techniques and Applications with Examples

  • ID: 4530595
  • Book
  • 384 Pages
  • John Wiley and Sons Ltd
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A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences

Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniques in optimization that encompass the broadness and diversity of the methods (traditional and new) and algorithms. The author a noted expert in the field covers a wide range of topics including mathematical foundations, optimization formulation, optimality conditions, algorithmic complexity, linear programming, convex optimization, and integer programming.  In addition, the book discusses artificial neural network, clustering and classifications, constraint–handling, queueing theory, support vector machine and multi–objective optimization, evolutionary computation, nature–inspired algorithms and many other topics. 

Designed as a practical resource, all topics are explained in detail with step–by–step examples to show how each method works. The book s exercises test the acquired knowledge that can be potentially applied to real problem solving. By taking an informal approach to the subject, the author helps readers to rapidly acquire the basic knowledge in optimization, operational research, and applied data mining.  This important resource:

  • Offers an accessible and state–of–the–art introduction to the main optimization techniques
  • Contains both traditional optimization techniques and the most current algorithms and swarm intelligence–based techniques
  • Presents a balance of theory, algorithms, and implementation
  • Includes more than 100 worked examples with step–by–step explanations

Written for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, Optimization Techniques and Applications with Examples is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization.

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List of Figures xv

List of Tables xix

Preface xxi

Acknowledgments xxiii

Acronyms xxv

Introduction xxvii

References xxxiii

PART I FUNDAMENTALS

1 Mathematical Foundations 3

1.1 Functions and Continuity 3

1.1.1 Functions 3

1.1.2 Continuity 4

1.1.3 Upper and Lower Bounds 5

1.2 Review of Calculus 6

1.2.1 Differentiation 6

1.2.2 Taylor Expansions 9

1.2.3 Partial Derivatives 12

1.2.4 Lipschitz Continuity 14

1.2.5 Integration 14

1.3 Vectors 17

1.3.1 Vector Algebra 17

1.3.2 Norms 18

1.3.3 2D Norms 19

1.4 Matrix Algebra 19

1.4.1 Matrices 19

1.4.2 Determinant 24

1.4.3 Rank of a Matrix 24

1.4.4 Frobenius Norm 25

1.5 Eigenvalues and Eigenvectors 26

1.5.1 Definiteness 29

1.5.2 Quadratic Form 30

1.6 Optimization and Optimality 31

1.6.1 Minimum and Maximum 31

1.6.2 Feasible Solution 32

1.6.3 Gradient and Hessian Matrix 33

1.6.4 Optimality Conditions 35

1.7 General Formulation of Optimization Problems 36

Exercises 36

References 37

2 Algorithms, Complexity and Convexity 39

2.1 What is an Algorithm? 39

2.2 Order Notations 41

2.3 Convergence Rate 43

2.4 Computational Complexity 44

2.4.1 Time and Space Complexity 44

2.4.2 Class P 45

2.4.3 Class NP 46

2.4.4 NP–Completeness 46

2.4.5 Complexity of Algorithms 47

2.5 Convexity 47

2.5.1 Linear and Affine Functions 47

2.5.2 Convex Functions 50

2.5.3 Subgradients 52

2.6 Stochastic Nature in Algorithms 53

2.6.1 Algorithms with Randomization 53

2.6.2 Random Variables 53

2.6.3 Poisson Distribution and Gaussian Distribution 56

2.6.4 Monte Carlo 58

2.6.5 Common Probability Distributions 60

Exercises 63

References 63

PART II OPTIMISATION TECHNIQUES AND ALGORITHMS

3 Optimization 67

3.1 Unconstrained Optimization 67

3.1.1 Univariate Functions 67

3.1.2 Multivariate Functions 70

3.2 Gradient–Based Methods 73

3.2.1 Newton s Method 73

3.2.2 Convergence Analysis 74

3.2.3 Steepest Descent Method 76

3.2.4 Line Search 80

3.2.5 Conjugate Gradient Method 80

3.2.6 Stochastic Gradient Descent 82

3.2.7 Subgradient Method 83

3.3 Gradient–Free Nelder–Mead Method 83

3.3.1 A Simplex 83

3.3.2 Nelder–Mead Downhill Simplex Method 84

Exercises 86

References 86

4 Constrained Optimization 89

4.1 Mathematical Formulation 89

4.2 Lagrange Multipliers 90

4.3 Slack Variables 93

4.4 Generalized Reduced Gradient Method 97

4.5 KKT Conditions 99

4.6 Penalty Method 102

Exercises 103

References 104

5 Optimization Techniques: Approximation Methods 105

5.1 BFGS Method 105

5.2 Trust–Region Method 107

5.3 Sequential Quadratic Programming 109

5.3.1 Quadratic Programming 109

5.3.2 Sequential Quadratic Programming 109

5.4 Convex Optimization 111

5.5 Equality Constrained Optimization 115

5.6 Barrier Functions 117

5.7 Interior–Point Methods 121

5.8 Stochastic and Robust Optimization 122

Exercises 124

References 125

PART III APPLIED OPTIMIZATION

6 Linear Programming 129


6.1 Linear Programming 129

6.2 Simplex Method 131

6.2.1 Slack Variables 131

6.2.2 Standard Formulation 132

6.2.3 Duality 133

6.2.4 Augmented Form 134

6.3 Worked Example by Simplex Method 135

6.4 Interior–Point Method for LP 138

Exercises 141

References 142

7 Integer Programming 143

7.1 Integer Linear Programming 143

7.1.1 Review of LP 143

7.1.2 Integer Linear Programming 144

7.2 LP Relaxation 145

7.3 Branch and Bound 148

7.3.1 Example 1 150

7.3.2 Example 2 152

7.3.3 How to Branch 155

7.4 Mixed Integer Programming 157

7.5 Applications of LP, IP and MIP 158

7.5.1 Transport Problem 158

7.5.2 Product Portfolio 160

7.5.3 Scheduling 162

7.5.4 Knapsack Problem 163

7.5.5 Travelling Salesman Problem 163

Exercises 164

References 165

8 Regression and Regularization 167

8.1 Sample Mean and Variance 167

8.2 Regression Analysis 170

8.2.1 Maximum Likelihood 170

8.2.2 Regression 170

8.2.3 Linearization 175

8.2.4 Generalized Linear Regression 177

8.2.5 Goodness of Fit 181

8.3 Nonlinear Least Squares 181

8.3.1 Gauss–Newton Algorithm 182

8.3.2 Levenberg–Marquardt Algorithm 184

8.3.3 Weighted Least Squares 185

8.4 Over–fitting and Information Criteria 186

8.5 Regularization and Lasso Method 188

8.6 Logistic Regression 190

8.7 Principal Component Analysis 193

Exercises 197

References 198

9 Machine Learning Algorithms 201

9.1 Data Mining 201

9.1.1 Hierarchy Clustering 202

9.1.2 K–Means Clustering 203

9.1.3 Distance Metric 204

9.2 Data Mining for Big Data 205

9.2.1 Characteristics of Big Data 205

9.2.2 Statistical Nature of Big Data 205

9.2.3 Mining Big Data 206

9.3 Artificial Neural Networks 208

9.3.1 Neuron Model 209

9.3.2 Artificial Neural Networks 210

9.3.3 Back Propagation Algorithm 212

9.3.4 Loss Functions in ANN 214

9.3.5 Stochastic Gradient Descent 215

9.3.6 Restricted Boltzmann Machine 216

9.4 Support Vector Machine 217

9.4.1 Statistical Learning Theory 218

9.4.2 Linear Support Vector Machine 219

9.4.3 Kernel Functions and Nonlinear SVM 222

9.5 Deep Learning 223

9.5.1 Learning 223

9.5.2 Deep Neural Nets 224

9.5.3 Tuning of Hyper–Parameters 224

Exercises 225

References 225

10 Queueing Theory and Simulation 229

10.1 Introduction 229

10.1.1 Components of Queueing 229\

10.1.2 Notations 231

10.2 Arrival Model 232

10.2.1 Poisson Distribution 232

10.2.2 Inter–arrival Time 235

10.3 Service Model 235

10.3.1 Exponential Distribution 236

10.3.2 Service Time Model 237

10.3.3 Erlang Distribution 238

10.4 Basic Queueing Model 238

10.4.1 M/M/1 Queue 238

10.4.2 M/M/s Queue 243

10.5 Little s Law 245

10.6 Queue Management and Optimization 246

Exercises 247

References 248

PART IV MULTIOBJECTIVE OPTIMIZATION

11 Multiobjective Optimization 253

11.1 Introduction 253

11.2 Pareto Front and Pareto Optimality 255

11.3 Choice and Challenges 257

11.4 Transformation to Single Objective Optimization 258

11.4.1 Weighted Sum Method 258

11.4.2 Utility Function 261

11.5 The Constraint Method 263

11.6 Evolutionary Approaches 266

11.6.1 Metaheuristics 266

11.6.2 Non–Dominated Sorting Genetic Algorithm 267

Exercises 268

References 268

12 Constraint–Handling Techniques 271

12.1 Introduction and Overview 271

12.2 Method of Lagrange Multipliers 273

12.3 Barrier Function Method 274

12.4 Penalty Method 274

12.5 Equality Constraints via Tolerance 275

12.6 Feasibility Criteria 276

12.7 Stochastic Ranking 276

12.8 Multiobjective Constraint–Handling and Ranking 277

Exercises 278

References 278

PART V EVOLUTIONARY COMPUTATION AND NATUREINSPIRED ALGORITHMS

13 Evolutionary Algorithms 283

13.1 Evolutionary Computation 283

13.2 Evolutionary Strategy 284

13.3 Genetic Algorithms 285

13.3.1 Basic Procedure 286

13.3.2 Choice of Parameters 288

13.4 Simulated Annealing 289

13.5 Differential Evolution 292

Exercises 295

References 295

14 Nature–Inspired Algorithms 299

14.1 Introduction to Swarm Intelligence 299

14.2 Ant and Bee Algorithms 300

14.3 Particle Swarm Optimization 301

14.3.1 Accelerated PSO 303

14.3.2 Binary PSO 304

14.4 Firefly Algorithm 305

14.5 Cuckoo Search 308

14.5.1 Cuckoo Search Algorithm 309

14.5.2 L´evy Flight 311

14.5.3 Advantages of CS 314

14.6 Bat Algorithm 314

14.7 Flower Pollination Algorithm 317

14.8 Other Algorithms 320

Exercises 320

References 321

A Notes on Software Packages 323

A.1 Software Packages 323

A.2 Matlab Codes in This Book 324

A.3 Optimization Software 325

A.4 Data Mining Software 326

A.5 Machine Learning Software 326

B Problem Solutions 329

References 345

Index 357

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