{"product_id":"optimization-techniques-and-applications-with-examples-isbn-9781119490548","title":"Optimization Techniques and Applications with Examples","description":"\u003cp\u003e\u003cb\u003eA guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences\u003c\/b\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eOptimization Techniques and Applications with Examples\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eDesigned 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eOffers an accessible and state-of-the-art introduction to the main optimization techniques\u003c\/li\u003e \u003cli\u003eContains both traditional optimization techniques and the most current algorithms and swarm intelligence-based techniques\u003c\/li\u003e \u003cli\u003ePresents a balance of theory, algorithms, and implementation\u003c\/li\u003e \u003cli\u003eIncludes more than 100 worked examples with step-by-step explanations \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWritten for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, \u003ci\u003eOptimization Techniques and Applications with Examples\u003c\/i\u003e is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization.\u003c\/p\u003e \u003cp\u003eList of Figures xiii\u003c\/p\u003e \u003cp\u003eList of Tables xvii\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAcknowledgements xxi\u003c\/p\u003e \u003cp\u003eAcronyms xxiii\u003c\/p\u003e \u003cp\u003eIntroduction xxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Fundamentals 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Mathematical Foundations 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Functions and Continuity 3\u003c\/p\u003e \u003cp\u003e1.1.1 Functions 3\u003c\/p\u003e \u003cp\u003e1.1.2 Continuity 4\u003c\/p\u003e \u003cp\u003e1.1.3 Upper and Lower Bounds 4\u003c\/p\u003e \u003cp\u003e1.2 Review of Calculus 6\u003c\/p\u003e \u003cp\u003e1.2.1 Differentiation 6\u003c\/p\u003e \u003cp\u003e1.2.2 Taylor Expansions 9\u003c\/p\u003e \u003cp\u003e1.2.3 Partial Derivatives 12\u003c\/p\u003e \u003cp\u003e1.2.4 Lipschitz Continuity 13\u003c\/p\u003e \u003cp\u003e1.2.5 Integration 14\u003c\/p\u003e \u003cp\u003e1.3 Vectors 16\u003c\/p\u003e \u003cp\u003e1.3.1 Vector Algebra 17\u003c\/p\u003e \u003cp\u003e1.3.2 Norms 17\u003c\/p\u003e \u003cp\u003e1.3.3 2D Norms 19\u003c\/p\u003e \u003cp\u003e1.4 Matrix Algebra 19\u003c\/p\u003e \u003cp\u003e1.4.1 Matrices 19\u003c\/p\u003e \u003cp\u003e1.4.2 Determinant 23\u003c\/p\u003e \u003cp\u003e1.4.3 Rank of a Matrix 24\u003c\/p\u003e \u003cp\u003e1.4.4 Frobenius Norm 25\u003c\/p\u003e \u003cp\u003e1.5 Eigenvalues and Eigenvectors 25\u003c\/p\u003e \u003cp\u003e1.5.1 Definiteness 28\u003c\/p\u003e \u003cp\u003e1.5.2 Quadratic Form 29\u003c\/p\u003e \u003cp\u003e1.6 Optimization and Optimality 31\u003c\/p\u003e \u003cp\u003e1.6.1 Minimum and Maximum 31\u003c\/p\u003e \u003cp\u003e1.6.2 Feasible Solution 32\u003c\/p\u003e \u003cp\u003e1.6.3 Gradient and Hessian Matrix 32\u003c\/p\u003e \u003cp\u003e1.6.4 Optimality Conditions 34\u003c\/p\u003e \u003cp\u003e1.7 General Formulation of Optimization Problems 35\u003c\/p\u003e \u003cp\u003eExercises 36\u003c\/p\u003e \u003cp\u003eFurther Reading 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Algorithms, Complexity, and Convexity 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 What Is an Algorithm? 37\u003c\/p\u003e \u003cp\u003e2.2 Order Notations 39\u003c\/p\u003e \u003cp\u003e2.3 Convergence Rate 40\u003c\/p\u003e \u003cp\u003e2.4 Computational Complexity 42\u003c\/p\u003e \u003cp\u003e2.4.1 Time and Space Complexity 42\u003c\/p\u003e \u003cp\u003e2.4.2 Class P 43\u003c\/p\u003e \u003cp\u003e2.4.3 Class NP 44\u003c\/p\u003e \u003cp\u003e2.4.4 NP-Completeness 44\u003c\/p\u003e \u003cp\u003e2.4.5 Complexity of Algorithms 45\u003c\/p\u003e \u003cp\u003e2.5 Convexity 46\u003c\/p\u003e \u003cp\u003e2.5.1 Linear and Affine Functions 46\u003c\/p\u003e \u003cp\u003e2.5.2 Convex Functions 48\u003c\/p\u003e \u003cp\u003e2.5.3 Subgradients 50\u003c\/p\u003e \u003cp\u003e2.6 Stochastic Nature in Algorithms 51\u003c\/p\u003e \u003cp\u003e2.6.1 Algorithms with Randomization 51\u003c\/p\u003e \u003cp\u003e2.6.2 Random Variables 51\u003c\/p\u003e \u003cp\u003e2.6.3 Poisson Distribution and Gaussian Distribution 54\u003c\/p\u003e \u003cp\u003e2.6.4 Monte Carlo 56\u003c\/p\u003e \u003cp\u003e2.6.5 Common Probability Distributions 58\u003c\/p\u003e \u003cp\u003eExercises 61\u003c\/p\u003e \u003cp\u003eBibliography 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Optimization Techniques and Algorithms 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Optimization 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Unconstrained Optimization 65\u003c\/p\u003e \u003cp\u003e3.1.1 Univariate Functions 65\u003c\/p\u003e \u003cp\u003e3.1.2 Multivariate Functions 68\u003c\/p\u003e \u003cp\u003e3.2 Gradient-Based Methods 70\u003c\/p\u003e \u003cp\u003e3.2.1 Newton’s Method 71\u003c\/p\u003e \u003cp\u003e3.2.2 Convergence Analysis 72\u003c\/p\u003e \u003cp\u003e3.2.3 Steepest Descent Method 73\u003c\/p\u003e \u003cp\u003e3.2.4 Line Search 77\u003c\/p\u003e \u003cp\u003e3.2.5 Conjugate Gradient Method 78\u003c\/p\u003e \u003cp\u003e3.2.6 Stochastic Gradient Descent 79\u003c\/p\u003e \u003cp\u003e3.2.7 Subgradient Method 81\u003c\/p\u003e \u003cp\u003e3.3 Gradient-Free Nelder–Mead Method 81\u003c\/p\u003e \u003cp\u003e3.3.1 A Simplex 81\u003c\/p\u003e \u003cp\u003e3.3.2 Nelder–Mead Downhill Simplex Method 82\u003c\/p\u003e \u003cp\u003eExercises 84\u003c\/p\u003e \u003cp\u003eBibliography 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Constrained Optimization 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Mathematical Formulation 87\u003c\/p\u003e \u003cp\u003e4.2 Lagrange Multipliers 87\u003c\/p\u003e \u003cp\u003e4.3 Slack Variables 91\u003c\/p\u003e \u003cp\u003e4.4 Generalized Reduced Gradient Method 94\u003c\/p\u003e \u003cp\u003e4.5 KKT Conditions 97\u003c\/p\u003e \u003cp\u003e4.6 PenaltyMethod 99\u003c\/p\u003e \u003cp\u003eExercises 101\u003c\/p\u003e \u003cp\u003eBibliography 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Optimization Techniques: Approximation Methods 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 BFGS Method 103\u003c\/p\u003e \u003cp\u003e5.2 Trust-Region Method 105\u003c\/p\u003e \u003cp\u003e5.3 Sequential Quadratic Programming 107\u003c\/p\u003e \u003cp\u003e5.3.1 Quadratic Programming 107\u003c\/p\u003e \u003cp\u003e5.3.2 SQP Procedure 107\u003c\/p\u003e \u003cp\u003e5.4 Convex Optimization 109\u003c\/p\u003e \u003cp\u003e5.5 Equality Constrained Optimization 113\u003c\/p\u003e \u003cp\u003e5.6 Barrier Functions 115\u003c\/p\u003e \u003cp\u003e5.7 Interior-PointMethods 119\u003c\/p\u003e \u003cp\u003e5.8 Stochastic and Robust Optimization 121\u003c\/p\u003e \u003cp\u003eExercises 123\u003c\/p\u003e \u003cp\u003eBibliography 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Applied Optimization 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Linear Programming 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 127\u003c\/p\u003e \u003cp\u003e6.2 Simplex Method 129\u003c\/p\u003e \u003cp\u003e6.2.1 Slack Variables 129\u003c\/p\u003e \u003cp\u003e6.2.2 Standard Formulation 130\u003c\/p\u003e \u003cp\u003e6.2.3 Duality 131\u003c\/p\u003e \u003cp\u003e6.2.4 Augmented Form 132\u003c\/p\u003e \u003cp\u003e6.3 Worked Example by Simplex Method 133\u003c\/p\u003e \u003cp\u003e6.4 Interior-PointMethod for LP 136\u003c\/p\u003e \u003cp\u003eExercises 139\u003c\/p\u003e \u003cp\u003eBibliography 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Integer Programming 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Integer Linear Programming 141\u003c\/p\u003e \u003cp\u003e7.1.1 Review of LP 141\u003c\/p\u003e \u003cp\u003e7.1.2 Integer LP 142\u003c\/p\u003e \u003cp\u003e7.2 LP Relaxation 143\u003c\/p\u003e \u003cp\u003e7.3 Branch and Bound 146\u003c\/p\u003e \u003cp\u003e7.3.1 How to Branch 153\u003c\/p\u003e \u003cp\u003e7.4 Mixed Integer Programming 155\u003c\/p\u003e \u003cp\u003e7.5 Applications of LP, IP, and MIP 156\u003c\/p\u003e \u003cp\u003e7.5.1 Transport Problem 156\u003c\/p\u003e \u003cp\u003e7.5.2 Product Portfolio 158\u003c\/p\u003e \u003cp\u003e7.5.3 Scheduling 160\u003c\/p\u003e \u003cp\u003e7.5.4 Knapsack Problem 161\u003c\/p\u003e \u003cp\u003e7.5.5 Traveling Salesman Problem 161\u003c\/p\u003e \u003cp\u003eExercises 163\u003c\/p\u003e \u003cp\u003eBibliography 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Regression and Regularization 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Sample Mean and Variance 165\u003c\/p\u003e \u003cp\u003e8.2 Regression Analysis 168\u003c\/p\u003e \u003cp\u003e8.2.1 Maximum Likelihood 168\u003c\/p\u003e \u003cp\u003e8.2.2 Regression 168\u003c\/p\u003e \u003cp\u003e8.2.3 Linearization 173\u003c\/p\u003e \u003cp\u003e8.2.4 Generalized Linear Regression 175\u003c\/p\u003e \u003cp\u003e8.2.5 Goodness of Fit 178\u003c\/p\u003e \u003cp\u003e8.3 Nonlinear Least Squares 179\u003c\/p\u003e \u003cp\u003e8.3.1 Gauss–Newton Algorithm 180\u003c\/p\u003e \u003cp\u003e8.3.2 Levenberg–Marquardt Algorithm 182\u003c\/p\u003e \u003cp\u003e8.3.3 Weighted Least Squares 183\u003c\/p\u003e \u003cp\u003e8.4 Over-fitting and Information Criteria 184\u003c\/p\u003e \u003cp\u003e8.5 Regularization and Lasso Method 186\u003c\/p\u003e \u003cp\u003e8.6 Logistic Regression 187\u003c\/p\u003e \u003cp\u003e8.7 Principal Component Analysis 191\u003c\/p\u003e \u003cp\u003eExercises 195\u003c\/p\u003e \u003cp\u003eBibliography 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Machine Learning Algorithms 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Data Mining 199\u003c\/p\u003e \u003cp\u003e9.1.1 Hierarchy Clustering 200\u003c\/p\u003e \u003cp\u003e9.1.2 k-Means Clustering 201\u003c\/p\u003e \u003cp\u003e9.1.3 Distance Metric 202\u003c\/p\u003e \u003cp\u003e9.2 Data Mining for Big Data 202\u003c\/p\u003e \u003cp\u003e9.2.1 Characteristics of Big Data 203\u003c\/p\u003e \u003cp\u003e9.2.2 Statistical Nature of Big Data 203\u003c\/p\u003e \u003cp\u003e9.2.3 Mining Big Data 204\u003c\/p\u003e \u003cp\u003e9.3 Artificial Neural Networks 206\u003c\/p\u003e \u003cp\u003e9.3.1 Neuron Model 207\u003c\/p\u003e \u003cp\u003e9.3.2 Neural Networks 208\u003c\/p\u003e \u003cp\u003e9.3.3 Back Propagation Algorithm 210\u003c\/p\u003e \u003cp\u003e9.3.4 Loss Functions in ANN 212\u003c\/p\u003e \u003cp\u003e9.3.5 Stochastic Gradient Descent 213\u003c\/p\u003e \u003cp\u003e9.3.6 Restricted Boltzmann Machine 214\u003c\/p\u003e \u003cp\u003e9.4 Support Vector Machines 216\u003c\/p\u003e \u003cp\u003e9.4.1 Statistical Learning Theory 216\u003c\/p\u003e \u003cp\u003e9.4.2 Linear Support Vector Machine 217\u003c\/p\u003e \u003cp\u003e9.4.3 Kernel Functions and Nonlinear SVM 220\u003c\/p\u003e \u003cp\u003e9.5 Deep Learning 221\u003c\/p\u003e \u003cp\u003e9.5.1 Learning 221\u003c\/p\u003e \u003cp\u003e9.5.2 Deep Neural Nets 222\u003c\/p\u003e \u003cp\u003e9.5.3 Tuning of Hyper-Parameters 223\u003c\/p\u003e \u003cp\u003eExercises 223\u003c\/p\u003e \u003cp\u003eBibliography 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Queueing Theory and Simulation 227\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 227\u003c\/p\u003e \u003cp\u003e10.1.1 Components of Queueing 227\u003c\/p\u003e \u003cp\u003e10.1.2 Notations 228\u003c\/p\u003e \u003cp\u003e10.2 Arrival Model 230\u003c\/p\u003e \u003cp\u003e10.2.1 Poisson Distribution 230\u003c\/p\u003e \u003cp\u003e10.2.2 Inter-arrival Time 233\u003c\/p\u003e \u003cp\u003e10.3 Service Model 233\u003c\/p\u003e \u003cp\u003e10.3.1 Exponential Distribution 233\u003c\/p\u003e \u003cp\u003e10.3.2 Service Time Model 235\u003c\/p\u003e \u003cp\u003e10.3.3 Erlang Distribution 235\u003c\/p\u003e \u003cp\u003e10.4 Basic QueueingModel 236\u003c\/p\u003e \u003cp\u003e10.4.1 M\/M\/1 Queue 236\u003c\/p\u003e \u003cp\u003e10.4.2 M\/M\/s Queue 240\u003c\/p\u003e \u003cp\u003e10.5 Little’s Law 242\u003c\/p\u003e \u003cp\u003e10.6 Queue Management and Optimization 243\u003c\/p\u003e \u003cp\u003eExercises 245\u003c\/p\u003e \u003cp\u003eBibliography 246\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Advanced Topics 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Multiobjective Optimization 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 251\u003c\/p\u003e \u003cp\u003e11.2 Pareto Front and Pareto Optimality 253\u003c\/p\u003e \u003cp\u003e11.3 Choice and Challenges 255\u003c\/p\u003e \u003cp\u003e11.4 Transformation to Single Objective Optimization 256\u003c\/p\u003e \u003cp\u003e11.4.1 Weighted Sum Method 256\u003c\/p\u003e \u003cp\u003e11.4.2 Utility Function 259\u003c\/p\u003e \u003cp\u003e11.5 The 𝜖-Constraint Method 261\u003c\/p\u003e \u003cp\u003e11.6 Evolutionary Approaches 264\u003c\/p\u003e \u003cp\u003e11.6.1 Metaheuristics 264\u003c\/p\u003e \u003cp\u003e11.6.2 Non-Dominated Sorting Genetic Algorithm 265\u003c\/p\u003e \u003cp\u003eExercises 266\u003c\/p\u003e \u003cp\u003eBibliography 266\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Constraint-Handling Techniques 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction and Overview 269\u003c\/p\u003e \u003cp\u003e12.2 Method of Lagrange Multipliers 270\u003c\/p\u003e \u003cp\u003e12.3 Barrier Function Method 272\u003c\/p\u003e \u003cp\u003e12.4 PenaltyMethod 272\u003c\/p\u003e \u003cp\u003e12.5 Equality Constraints via Tolerance 273\u003c\/p\u003e \u003cp\u003e12.6 Feasibility Criteria 274\u003c\/p\u003e \u003cp\u003e12.7 Stochastic Ranking 275\u003c\/p\u003e \u003cp\u003e12.8 Multiobjective Constraint-Handling and Ranking 276\u003c\/p\u003e \u003cp\u003eExercises 276\u003c\/p\u003e \u003cp\u003eBibliography 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Evolutionary Computation and Nature-Inspired\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAlgorithms 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Evolutionary Algorithms 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Evolutionary Computation 281\u003c\/p\u003e \u003cp\u003e13.3.1 Basic Procedure 284\u003c\/p\u003e \u003cp\u003e13.3.2 Choice of Parameters 285\u003c\/p\u003e \u003cp\u003e13.4 Simulated Annealing 287\u003c\/p\u003e \u003cp\u003e13.5 Differential Evolution 290\u003c\/p\u003e \u003cp\u003eExercises 293\u003c\/p\u003e \u003cp\u003eBibliography 293\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Nature-Inspired Algorithms 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction to SI 297\u003c\/p\u003e \u003cp\u003e14.2 Ant and Bee Algorithms 298\u003c\/p\u003e \u003cp\u003e14.3 Particle Swarm Optimization 299\u003c\/p\u003e \u003cp\u003e14.3.1 Accelerated PSO 301\u003c\/p\u003e \u003cp\u003e14.3.2 Binary PSO 302\u003c\/p\u003e \u003cp\u003e14.4 Firefly Algorithm 303\u003c\/p\u003e \u003cp\u003e14.5 Cuckoo Search 306\u003c\/p\u003e \u003cp\u003e14.5.1 CS Algorithm 307\u003c\/p\u003e \u003cp\u003e14.5.2 Lévy Flight 309\u003c\/p\u003e \u003cp\u003e14.5.3 Advantages of CS 312\u003c\/p\u003e \u003cp\u003e14.6 Bat Algorithm 313\u003c\/p\u003e \u003cp\u003e14.7 Flower Pollination Algorithm 315\u003c\/p\u003e \u003cp\u003e14.8 Other Algorithms 319\u003c\/p\u003e \u003cp\u003eExercises 319\u003c\/p\u003e \u003cp\u003eBibliography 319\u003c\/p\u003e \u003cp\u003eAppendix A Notes on Software Packages 323\u003c\/p\u003e \u003cp\u003eAppendix B Problem Solutions 329\u003c\/p\u003e \u003cp\u003eIndex 345\u003c\/p\u003e \t \u003cp\u003e\u003cb\u003eXIN-SHE YANG, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Reader\/Professor in Modelling and Optimization at Middlesex University London. He is also an elected Bye-Fellow and College Lecturer at Cambridge University, Adjunct Professor at Reykjavik University, Iceland, as well as Distinguished Chair Professor at Xi'an Polytechnic University, China.  \t \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA Guide to Modern Optimization Applications and Techniques in Newly Emerging Areas Spanning Optimization, Data Science, Machine Intelligence, Engineering, and Computer Sciences\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eOptimization Techniques and Applications with Examples\u003c\/i\u003e 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.  \u003c\/p\u003e\u003cp\u003eDesigned 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:  \u003c\/p\u003e\u003cli\u003eOffers an accessible and state-of-the-art introduction to the main optimization techniques\u003c\/li\u003e \u003cli\u003eContains both traditional optimization techniques and the most current algorithms and swarm intelligence-based techniques\u003c\/li\u003e \u003cli\u003ePresents a balance of theory, algorithms, and implementation\u003c\/li\u003e \u003cli\u003eIncludes more than 100 worked examples with step-by-step explanations\u003c\/li\u003e  \u003cp\u003eWritten for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, \u003ci\u003eOptimization Techniques and Applications with Examples\u003c\/i\u003e is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989723037925,"sku":"NP9781119490548","price":133.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119490548.jpg?v=1761785252","url":"https:\/\/k12savings.com\/products\/optimization-techniques-and-applications-with-examples-isbn-9781119490548","provider":"K12savings","version":"1.0","type":"link"}