Optimization techniques and applications with examples
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 techniquesin op...
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| Main Author | |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Hoboken, N.J
Wiley
2018
John Wiley & Sons, Incorporated Wiley-Blackwell |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9781119490548 1119490545 1119490626 9781119490623 |
| DOI | 10.1002/9781119490616 |
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| Abstract | 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 techniquesin 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 techniquesin optimization. |
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| AbstractList | 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 techniquesin 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 techniquesin optimization. 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. |
| Author | Yang, Xin-She |
| Author_xml | – sequence: 1 fullname: Yang, Xin-She |
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| Notes | Includes bibliographical references and index |
| OCLC | 1044778107 |
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| PublicationPlace | Hoboken, N.J |
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| Publisher | Wiley John Wiley & Sons, Incorporated Wiley-Blackwell |
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| Snippet | A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and... |
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| SubjectTerms | Mathematical optimization |
| TableOfContents | 3.2.3 Steepest Descent Method -- 3.2.4 Line Search -- 3.2.5 Conjugate Gradient Method -- 3.2.6 Stochastic Gradient Descent -- 3.2.7 Subgradient Method -- 3.3 Gradient-Free Nelder-Mead Method -- 3.3.1 A Simplex -- 3.3.2 Nelder-Mead Downhill Simplex Method -- Exercises -- Bibliography -- Chapter 4 Constrained Optimization -- 4.1 Mathematical Formulation -- 4.2 Lagrange Multipliers -- 4.3 Slack Variables -- 4.4 Generalized Reduced GradientMethod -- 4.5 KKT Conditions -- 4.6 Penalty Method -- Exercises -- Bibliography -- Chapter 5 Optimization Techniques: Approximation Methods -- 5.1 BFGS Method -- 5.2 Trust-Region Method -- 5.2 Trust-Region Method -- 5.3 Sequential Quadratic Programming -- 5.3.1 Quadratic Programming -- 5.3.2 SQP Procedure -- 5.4 Convex Optimization -- 5.5 Equality Constrained Optimization -- 5.6 Barrier Functions -- 5.7 Interior-Point Methods -- 5.8 Stochastic and Robust Optimization -- Exercises -- Bibliography -- Part III Applied Optimization -- Chapter 6 Linear Programming -- 6.1 Introduction -- 6.2 Simplex Method -- 6.2.1 Slack Variables -- 6.2.2 Standard Formulation -- 6.2.3 Duality -- 6.3 Worked Example by Simplex Method -- 6.4 Interior-Point Method for LP -- Exercises -- Bibliography -- Chapter 7 Integer Programming -- 7.1 Integer Linear Programming -- 7.1.1 Review of LP -- 7.1.2 Integer LP -- 7.2 LP Relaxation -- 7.3 Branch and Bound -- 7.3.1 How to Branch -- 7.4 Mixed Integer Programming -- 7.5 Applications of LP, IP, and MIP -- 7.5.1 Transport Problem -- 7.5.2 Product Portfolio -- 7.5.3 Scheduling -- 7.5.4 Knapsack Problem -- 7.5.5 Traveling Salesman Problem -- Exercises -- Bibliography -- Chapter 8 Regression and Regularization -- 8.1 Sample Mean and Variance -- 8.2 Regression Analysis -- 8.2.1 Maximum Likelihood -- 8.2.2 Regression -- 8.2.3 Linearization -- 8.2.4 Generalized Linear Regression -- 8.2.5 Goodness of Fit 8.3 Nonlinear Least Squares -- 8.3.1 Gauss-Newton Algorithm -- 8.3.2 Levenberg-Marquardt Algorithm -- 8.3.3 Weighted Least Squares -- 8.4 Over-fitting and Information Criteria -- 8.5 Regularization and Lasso Method -- 8.6 Logistic Regression -- 8.7 Principal Component Analysis -- Exercises -- Bibliography -- Chapter 9 Machine Learning Algorithms -- 9.1 DataMining -- 9.1.1 Hierarchy Clustering -- 9.1.2 k-Means Clustering -- 9.1.3 DistanceMetric -- 9.2 DataMining for Big Data -- 9.2.1 Characteristics of Big Data -- 9.2.2 Statistical Nature of Big Data -- 9.2.3 Mining Big Data -- 9.3 Artificial Neural Networks -- 9.3.1 Neuron Model -- 9.3.2 Neural Networks -- 9.3.3 Back Propagation Algorithm -- 9.3.4 Loss Functions in ANN -- 9.3.5 Stochastic Gradient Descent -- 9.3.6 Restricted Boltzmann Machine -- 9.4 Support Vector Machines -- 9.4.1 Statistical Learning Theory -- 9.4.2 Linear Support Vector Machine -- 9.4.3 Kernel Functions and Nonlinear SVM -- 9.5 Deep Learning -- 9.5.1 Learning -- 9.5.2 Deep Neural Nets -- 9.5.3 Tuning of Hyper-Parameters -- Exercises -- Bibliography -- Chapter 10 Queueing Theory and Simulation -- 10.1 Introduction -- 10.1.1 Components of Queueing -- 10.1.2 Notations -- 10.2 Arrival Model -- 10.2.1 Poisson Distribution -- 10.2.2 Inter-arrival Time -- 10.3 Service Model -- 10.3.1 Exponential Distribution -- 10.3.2 Service Time Model -- 10.3.3 Erlang Distribution -- 10.4 Basic Queueing Model -- 10.4.1 M/M/1 Queue -- 10.4.2 M/M/s Queue -- 10.5 Little's Law -- 10.6 Queue Management and Optimization -- Exercises -- Bibliography -- Part IV Advanced Topics -- Chapter 11 Multiobjective Optimization -- 11.1 Introduction -- 11.2 Pareto Front and Pareto Optimality -- 11.3 Choice and Challenges -- 11.4 Transformation to Single Objective Optimization -- 11.4.1 Weighted SumMethod -- 11.4.2 Utility Function -- 11.5 The -Constraint Method Intro -- Title Page -- Copyright Page -- Contents -- List of Figures -- List of Tables -- Preface -- Acknowledgements -- Acronyms -- Introduction -- Part I Fundamentals -- Chapter 1 Mathematical Foundations -- 1.1 Functions and Continuity -- 1.1.1 Functions -- 1.1.2 Continuity -- 1.1.3 Upper and Lower Bounds -- 1.2 Review of Calculus -- 1.2.1 Differentiation -- 1.2.2 Taylor Expansions -- 1.2.3 Partial Derivatives -- 1.2.4 Lipschitz Continuity -- 1.2.5 Integration -- 1.3 Vectors -- 1.3.1 Vector Algebra -- 1.3.2 Norms -- 1.3.3 2D Norms -- 1.4 Matrix Algebra -- 1.4.1 Matrices -- 1.4.2 Determinant -- 1.4.3 Rank of a Matrix -- 1.4.4 Frobenius Norm -- 1.5 Eigenvalues and Eigenvectors -- 1.5.1 Definiteness -- 1.5.2 Quadratic Form -- 1.6 Optimization and Optimality -- 1.6.1 Minimum and Maximum -- 1.6.2 Feasible Solution -- 1.6.3 Gradient and Hessian Matrix -- 1.6.4 Optimality Conditions -- 1.7 General Formulation of Optimization Problems -- Exercises -- Further Reading -- Chapter 2 Algorithms, Complexity, and Convexity -- 2.1 What Is an Algorithm? -- 2.2 Order Notations -- 2.3 Convergence Rate -- 2.4 Computational Complexity -- 2.4.1 Time and Space Complexity -- 2.4.2 Class P -- 2.4.3 Class NP -- 2.4.4 NP-Completeness -- 2.4.5 Complexity of Algorithms -- 2.5 Convexity -- 2.5.1 Linear and Affine Functions -- 2.5.2 Convex Functions -- 2.5.3 Subgradients -- 2.6 Stochastic Nature in Algorithms -- 2.6.1 Algorithms with Randomization -- 2.6.2 Random Variables -- 2.6.3 Poisson Distribution and Gaussian Distribution -- 2.6.4 Monte Carlo -- 2.6.5 Common Probability Distributions -- Exercises -- Bibliography -- Part II Optimization Techniques and Algorithms -- Chapter 3 Optimization -- 3.1 Unconstrained Optimization -- 3.1.1 Univariate Functions -- 3.1.2 Multivariate Functions -- 3.2 Gradient-Based Methods -- 3.2.1 Newton's Method -- 3.2.2 Convergence Analysis 11.6 Evolutionary Approaches -- 11.6.1 Metaheuristics -- 11.6.2 Non-Dominated Sorting Genetic Algorithm -- Exercises -- Bibliography -- Chapter 12 Constraint-Handling Techniques -- 12.1 Introduction and Overview -- 12.2 Method of Lagrange Multipliers -- 12.3 Barrier Function Method -- 12.4 Penalty Method -- 12.5 Equality Constraints via Tolerance -- 12.6 Feasibility Criteria -- 12.7 Stochastic Ranking -- 12.8 Multiobjective Constraint-Handling and Ranking -- Exercises -- Bibliography -- Part V Evolutionary Computation and Nature-Inspired Algorithms -- Chapter 13 Evolutionary Algorithms -- 13.1 Evolutionary Computation -- 13.2 Evolutionary Strategy -- 13.3 Genetic Algorithms -- 13.3.1 Basic Procedure -- 13.3.2 Choice of Parameters -- 13.4 Simulated Annealing -- 13.5 Differential Evolution -- Exercises -- Bibliography -- Chapter 14 Nature-Inspired Algorithms -- 14.1 Introduction to SI -- 14.2 Ant and Bee Algorithms -- 14.3 Particle Swarm Optimization -- 14.3.1 Accelerated PSO -- 14.3.2 Binary PSO -- 14.4 Firefly Algorithm -- 14.5 Cuckoo Search -- 14.5.1 CS Algorithm -- 14.5.2 Lévy Flight -- 14.5.3 Advantages of CS -- 14.6 Bat Algorithm -- 14.7 Flower Pollination Algorithm -- 14.8 Other Algorithms -- Exercises -- Bibliography -- Appendix A Notes on Software Packages -- A.1 Software Packages -- A.2 Matlab Codes in This Book -- A.3 Optimization Software -- A.4 DataMining Software -- A.5 Machine Learning Software -- Appendix B Problem Solutions -- Solutions for Chapter 1 -- Solutions for Chapter 2 -- Solutions for Chapter 3 -- Solutions for Chapter 4 -- Solutions for Chapter 5 -- Solutions for Chapter 6 -- Solutions for Chapter 7 -- Solutions for Chapter 8 -- Solutions for Chapter 9 -- Solutions for Chapter 10 -- Solutions for Chapter 11 -- Solutions for Chapter 12 -- Solutions for Chapter 13 -- Solutions for Chapter 14 -- Index -- EULA |
| Title | Optimization techniques and applications with examples |
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