{"product_id":"engineering-optimization-methods-and-applications-hardback-9780471558149","title":"Engineering Optimization; Methods and Applications (Hardback) 9780471558149","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eEngineering Optimization\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eMethods and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eA. Ravindran (Author), Ken M. Ragsdell (Author), Gintaras V. Reklaitis (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471558149, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 16 June 2006\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e688 pages, Drawings: 150 B\u0026amp;W, 0 Color\u003cbr\u003e24.2 x 16.4 x 4 cm, 1.1 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThe classic introduction to engineering optimization theory and practice--now expanded and updated\u003cbr\u003e \u003cbr\u003e \u003cbr\u003e Engineering optimization helps engineers zero in on the most effective, efficient solutions to problems. This text provides a practical, real-world understanding of engineering optimization. Rather than belaboring underlying proofs and mathematical derivations, it emphasizes optimization methodology, focusing on techniques and stratagems relevant to engineering applications in design, operations, and analysis. It surveys diverse optimization methods, ranging from those applicable to the minimization of a single-variable function to those most suitable for large-scale, nonlinear constrained problems. New material covered includes the duality theory, interior point methods for solving LP problems, the generalized Lagrange multiplier method and generalization of convex functions, and goal programming for solving multi-objective optimization problems. A practical, hands-on reference and text, Engineering Optimization, Second Edition covers:\u003cbr\u003e * Practical issues, such as model formulation, implementation, starting point generation, and more\u003cbr\u003e * Current, state-of-the-art optimization software\u003cbr\u003e * Three engineering case studies plus numerous examples from chemical, industrial, and mechanical engineering\u003cbr\u003e * Both classical methods and new techniques, such as successive quadratic programming, interior point methods, and goal programming\u003cbr\u003e \u003cbr\u003e Excellent for self-study and as a reference for engineering professionals, this Second Edition is also ideal for senior and graduate courses on engineering optimization, including television and online instruction, as well as for in-plant training.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface.\u003cbr\u003e \u003cbr\u003e 1 Introduction to Optimization.\u003cbr\u003e \u003cbr\u003e 1.1 Requirements for the Application of Optimization Methods.\u003cbr\u003e \u003cbr\u003e 1.2 Applications of Optimization in Engineering.\u003cbr\u003e \u003cbr\u003e 1.3 Structure of Optimization Problems.\u003cbr\u003e \u003cbr\u003e 1.4 Scope of This Book.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e 2 Functions of a Single Variable.\u003cbr\u003e \u003cbr\u003e 2.1 Properties of Single-Variable Functions.\u003cbr\u003e \u003cbr\u003e 2.2 Optimality Criteria.\u003cbr\u003e \u003cbr\u003e 2.3 Region Elimination Methods.\u003cbr\u003e \u003cbr\u003e 2.4 Polynomial Approximation or Point Estimation Methods.\u003cbr\u003e \u003cbr\u003e 2.5 Methods Requiring Derivatives.\u003cbr\u003e \u003cbr\u003e 2.6 Comparison of Methods.\u003cbr\u003e \u003cbr\u003e 2.7 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Problems.\u003cbr\u003e \u003cbr\u003e 3 Functions of Several Variables.\u003cbr\u003e \u003cbr\u003e 3.1 Optimality Criteria.\u003cbr\u003e \u003cbr\u003e 3.2 Direct-Search Methods.\u003cbr\u003e \u003cbr\u003e 3.3 Gradient-Based Methods.\u003cbr\u003e \u003cbr\u003e 3.4 Comparison of Methods and Numerical Results.\u003cbr\u003e \u003cbr\u003e 3.5 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Problems.\u003cbr\u003e \u003cbr\u003e 4 Linear Programming.\u003cbr\u003e \u003cbr\u003e 4.1 Formulation of Linear Programming Models.\u003cbr\u003e \u003cbr\u003e 4.2 Graphical Solution of Linear Programs in Two Variables.\u003cbr\u003e \u003cbr\u003e 4.3 Linear Program in Standard Form.\u003cbr\u003e \u003cbr\u003e 4.5 Computer Solution of Linear Programs.\u003cbr\u003e \u003cbr\u003e 4.5.1 Computer Codes.\u003cbr\u003e \u003cbr\u003e 4.6 Sensitivity Analysis in Linear Programming.\u003cbr\u003e \u003cbr\u003e 4.7 Applications.\u003cbr\u003e \u003cbr\u003e 4.8 Additional Topics in Linear Programming.\u003cbr\u003e \u003cbr\u003e 4.9 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Problems.\u003cbr\u003e \u003cbr\u003e 5 Constrained Optimality Criteria.\u003cbr\u003e \u003cbr\u003e 5.1 Equality-Constrained Problems.\u003cbr\u003e \u003cbr\u003e 5.2 Lagrange Multipliers.\u003cbr\u003e \u003cbr\u003e 5.3 Economic Interpretation of Lagrange Multipliers.\u003cbr\u003e \u003cbr\u003e 5.4 Kuhn-Tucker Conditions.\u003cbr\u003e \u003cbr\u003e 5.5 Kuhn-Tucker Theorems.\u003cbr\u003e \u003cbr\u003e 5.6 Saddlepoint Conditions.\u003cbr\u003e \u003cbr\u003e 5.7 Second-Order Optimality Conditions.\u003cbr\u003e \u003cbr\u003e 5.8 Generalized Lagrange Multiplier Method.\u003cbr\u003e \u003cbr\u003e 5.9 Generalization of Convex Functions.\u003cbr\u003e \u003cbr\u003e 5.10 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Problems.\u003cbr\u003e \u003cbr\u003e 6 Transformation Methods.\u003cbr\u003e \u003cbr\u003e 6.1 Penalty Concept.\u003cbr\u003e \u003cbr\u003e 6.2 Algorithms, Codes, and Other Contributions.\u003cbr\u003e \u003cbr\u003e 6.3 Method of Multipliers.\u003cbr\u003e \u003cbr\u003e 6.4 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Problems.\u003cbr\u003e \u003cbr\u003e 7 Constrained Direct Search.\u003cbr\u003e \u003cbr\u003e 7.1 Problem Preparation.\u003cbr\u003e \u003cbr\u003e 7.2 Adaptations of Unconstrained Search Methods.\u003cbr\u003e \u003cbr\u003e 7.3 Random-Search Methods.\u003cbr\u003e \u003cbr\u003e 7.4 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Problems.\u003cbr\u003e \u003cbr\u003e 8 Linearization Methods for Constrained Problems.\u003cbr\u003e \u003cbr\u003e 8.1 Direct Use of Successive Linear Programs.\u003cbr\u003e \u003cbr\u003e 8.2 Separable Programming.\u003cbr\u003e \u003cbr\u003e 8.3 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Problems.\u003cbr\u003e \u003cbr\u003e 9 Direction Generation Methods Based on Linearization.\u003cbr\u003e \u003cbr\u003e 9.1 Method of Feasible Directions.\u003cbr\u003e \u003cbr\u003e 9.2 Simplex Extensions for Linearly Constrained Problems.\u003cbr\u003e \u003cbr\u003e 9.3 Generalized Reduced Gradient Method.\u003cbr\u003e \u003cbr\u003e 9.4 Design Application.\u003cbr\u003e \u003cbr\u003e 9.5 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Problems.\u003cbr\u003e \u003cbr\u003e 10 Quadratic Approximation Methods for Constrained Problems.\u003cbr\u003e \u003cbr\u003e 10.1 Direct Quadratic Approximation.\u003cbr\u003e \u003cbr\u003e 10.2 Quadratic Approximation of the Lagrangian Function.\u003cbr\u003e \u003cbr\u003e 10.3 Variable Metric Methods for Constrained Optimization.\u003cbr\u003e \u003cbr\u003e 10.4 Discussion.\u003cbr\u003e \u003cbr\u003e 10.5 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Problems.\u003cbr\u003e \u003cbr\u003e 11 Structured Problems and Algorithms.\u003cbr\u003e \u003cbr\u003e 11.1 Integer Programming.\u003cbr\u003e \u003cbr\u003e 11.2 Quadratic Programming.\u003cbr\u003e \u003cbr\u003e 11.3 Complementary Pivot Problems.\u003cbr\u003e \u003cbr\u003e 11.4 Goal Programming.\u003cbr\u003e \u003cbr\u003e 11.5 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Problems.\u003cbr\u003e \u003cbr\u003e 12 Comparison of Constrained Optimization Methods.\u003cbr\u003e \u003cbr\u003e 12.1 Software Availability.\u003cbr\u003e \u003cbr\u003e 12.2 A Comparison Philosophy.\u003cbr\u003e \u003cbr\u003e 12.3 Brief History of Classical Comparative Experiments.\u003cbr\u003e \u003cbr\u003e 12.4 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e 13 Strategies for Optimization Studies.\u003cbr\u003e \u003cbr\u003e 13.1 Model Formulation.\u003cbr\u003e \u003cbr\u003e 13.2 Problem Implementation.\u003cbr\u003e \u003cbr\u003e 13.3 Solution Evaluation.\u003cbr\u003e \u003cbr\u003e 13.4 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Problems.\u003cbr\u003e \u003cbr\u003e 14 Engineering Case Studies.\u003cbr\u003e \u003cbr\u003e 14.1 Optimal Location of Coal-Blending Plants by Mixed-Integer\u003cbr\u003e \u003cbr\u003e Programming.\u003cbr\u003e \u003cbr\u003e 14.2 Optimization of an Ethylene Glycol-Ethylene Oxide Process.\u003cbr\u003e \u003cbr\u003e 14.3 Optimal Design of a Compressed Air Energy Storage System.\u003cbr\u003e \u003cbr\u003e 14.4 Summary.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Appendix A Review of Linear Algebra.\u003cbr\u003e \u003cbr\u003e A.1 Set Theory.\u003cbr\u003e \u003cbr\u003e A.2 Vectors.\u003cbr\u003e \u003cbr\u003e A.3 Matrices.\u003cbr\u003e \u003cbr\u003e A.3.1 Matrix Operations.\u003cbr\u003e \u003cbr\u003e A.3.2 Determinant of a Square Matrix.\u003cbr\u003e \u003cbr\u003e A.3.3 Inverse of a Matrix.\u003cbr\u003e \u003cbr\u003e A.3.4 Condition of a Matrix.\u003cbr\u003e \u003cbr\u003e A.3.5 Sparse Matrix.\u003cbr\u003e \u003cbr\u003e A.4 Quadratic Forms.\u003cbr\u003e \u003cbr\u003e A.4.1 Principal Minor.\u003cbr\u003e \u003cbr\u003e A.4.2 Completing the Square.\u003cbr\u003e \u003cbr\u003e A.5 Convex Sets.\u003cbr\u003e \u003cbr\u003e Appendix B Convex and Concave Functions.\u003cbr\u003e \u003cbr\u003e Appendix C Gauss-Jordan Elimination Scheme.\u003cbr\u003e \u003cbr\u003e Author Index.\u003cbr\u003e \u003cbr\u003e Subject Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mechanical engineering \u0026amp; materials [\u003ca title=\"See our other books on Mechanical engineering \u0026amp; materials\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mechanical%20engineering%20\u0026amp;%20materials%20%5BTG%5D%22\"\u003eTG\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand 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