{"product_id":"nonlinear-programming-theory-and-algorithms-hardback-9780471486008","title":"Nonlinear Programming; Theory and Algorithms (Hardback) 9780471486008","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eNonlinear Programming\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTheory and Algorithms\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMokhtar S. Bazaraa (Author), Hanif D. Sherali (Author), C. M. Shetty (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471486008, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 June 2006\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e880 pages, Charts: 9 B\u0026amp;W, 0 Color; Drawings: 10 B\u0026amp;W, 0 Color; Graphs: 113 B\u0026amp;W, 0 Color\u003cbr\u003e23.9 x 16.3 x 5.3 cm, 1.406 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"The promotional message on the back cover proclaims 'this book is a solid reference for professionals and a useful text for students…\"; and I fully agree.\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, February 2007)  \u003cp\u003e\"Noted and recommended for its logical format and sharp editing that never wavers in its focus.\" (\u003ci\u003eElectric Review\u003c\/i\u003e, September\/October 2006)\u003c\/p\u003e \u003cp\u003e\"…highly recommended for a course in the theory of nonlinear programming…\" (\u003ci\u003eMAA Reviews\u003c\/i\u003e, July 17, 2006)\u003c\/p\u003e \u003cp\u003e ‘… ‘the Bazaraa’ is a must if you are interested in optimization…’ (\u003ci\u003eJournal of the Operational Research Society,\u003c\/i\u003e 2007)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eCOMPREHENSIVE COVERAGE OF NONLINEAR PROGRAMMING THEORY AND ALGORITHMS, THOROUGHLY REVISED AND EXPANDED\u003c\/b\u003e  \u003cp\u003e\u003ci\u003eNonlinear Programming: Theory and Algorithms\u003c\/i\u003e—now in an extensively updated Third Edition—addresses the problem of optimizing an objective function in the presence of equality and inequality constraints. Many realistic problems cannot be adequately represented as a linear program owing to the nature of the nonlinearity of the objective function and\/or the nonlinearity of any constraints. The \u003ci\u003eThird Edition\u003c\/i\u003e begins with a general introduction to nonlinear programming with illustrative examples and guidelines for model construction.\u003c\/p\u003e \u003cp\u003eConcentration on the three major parts of nonlinear programming is provided:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eConvex analysis with discussion of topological properties of convex sets, separation and support of convex sets, polyhedral sets, extreme points and extreme directions of polyhedral sets, and linear programming\u003c\/li\u003e \u003cli\u003eOptimality conditions and duality with coverage of the nature, interpretation, and value of the classical Fritz John (FJ) and the Karush-Kuhn-Tucker (KKT) optimality conditions; the interrelationships between various proposed constraint qualifications; and Lagrangian duality and saddle point optimality conditions\u003c\/li\u003e \u003cli\u003eAlgorithms and their convergence, with a presentation of algorithms for solving both unconstrained and constrained nonlinear programming problems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eImportant features of the \u003ci\u003eThird Edition\u003c\/i\u003e include:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eNew topics such as second interior point methods, nonconvex optimization, nondifferentiable optimization, and more\u003c\/li\u003e \u003cli\u003eUpdated discussion and new applications in each chapter\u003c\/li\u003e \u003cli\u003eDetailed numerical examples and graphical illustrations\u003c\/li\u003e \u003cli\u003eEssential coverage of modeling and formulating nonlinear programs\u003c\/li\u003e \u003cli\u003eSimple numerical problems\u003c\/li\u003e \u003cli\u003eAdvanced theoretical exercises\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe book is a solid reference for professionals as well as a useful text for students in the fields of operations research, management science, industrial engineering, applied mathematics, and also in engineering disciplines that deal with analytical optimization techniques. The logical and self-contained format uniquely covers nonlinear programming techniques with a great depth of information and an abundance of valuable examples and illustrations that showcase the most current advances in nonlinear problems.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eChapter 1 Introduction.\u003c\/b\u003e  \u003cp\u003e1.1 Problem Statement and Basic Definitions.\u003c\/p\u003e \u003cp\u003e1.2 Illustrative Examples.\u003c\/p\u003e \u003cp\u003e1.3 Guidelines for Model Construction.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003eNotes and References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1 Convex Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Convex Sets.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Convex Hulls.\u003c\/p\u003e \u003cp\u003e2.2 Closure and Interior of a Set.\u003c\/p\u003e \u003cp\u003e2.3 Weierstrass's Theorem.\u003c\/p\u003e \u003cp\u003e2.4 Separation and Support of Sets.\u003c\/p\u003e \u003cp\u003e2.5 Convex Cones and Polarity.\u003c\/p\u003e \u003cp\u003e2.6 Polyhedral Sets, Extreme Points, and Extreme Directions.\u003c\/p\u003e \u003cp\u003e2.7 Linear Programming and the Simplex Method.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003eNotes and References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Convex Functions and Generalizations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Definitions and Basic Properties.\u003c\/p\u003e \u003cp\u003e3.2 Subgradients of Convex Functions.\u003c\/p\u003e \u003cp\u003e3.3 Differentiable Convex Functions.\u003c\/p\u003e \u003cp\u003e3.4 Minima and Maxima of Convex Functions.\u003c\/p\u003e \u003cp\u003e3.5 Generalizations of Convex Functions.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003eNotes and References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2 Optimality Conditions and Duality.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 The Fritz John and Karush-Kuhn-Tucker Optimality Conditions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Unconstrained Problems.\u003c\/p\u003e \u003cp\u003e4.2 Problems Having Inequality Constraints.\u003c\/p\u003e \u003cp\u003e4.3 Problems Having Inequality and Equality Constraints.\u003c\/p\u003e \u003cp\u003e4.4 Second-Order Necessary and Sufficient Optimality Conditions for Constrained Problems.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003eNotes and References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Constraint Qualifications.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Cone of Tangents.\u003c\/p\u003e \u003cp\u003e5.2 Other Constraint Qualifications.\u003c\/p\u003e \u003cp\u003e5.3 Problems Having Inequality and Equality Constraints.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003eNotes and References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Lagrangian Duality and Saddle Point Optimality Conditions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Lagrangian Dual Problem.\u003c\/p\u003e \u003cp\u003e6.2 Duality Theorems and Saddle Point Optimality Conditions.\u003c\/p\u003e \u003cp\u003e6.3 Properties of the Dual Function.\u003c\/p\u003e \u003cp\u003e6.4 Formulating and Solving the Dual Problem\u003c\/p\u003e \u003cp\u003e6.5 Getting the Primal Solution.\u003c\/p\u003e \u003cp\u003e6.6 Linear and Quadratic Programs.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003eNotes and References.\u003c\/p\u003e \u003cp\u003ePart 3 Algorithms and Their Convergence.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 The Concept of an Algorithm.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Algorithms and Algorithmic Maps.\u003c\/p\u003e \u003cp\u003e7.2 Closed Maps and Convergence.\u003c\/p\u003e \u003cp\u003e7.3 Composition of Mappings.\u003c\/p\u003e \u003cp\u003e7.4 Comparison Among Algorithms.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003eNotes and References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Unconstrained Optimization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Line Search Without Using Derivatives.\u003c\/p\u003e \u003cp\u003e8.2 Line Search Using Derivatives.\u003c\/p\u003e \u003cp\u003e8.3 Some Practical Line Search Methods.\u003c\/p\u003e \u003cp\u003e8.4 Closedness of the Line Search Algorithmic Map.\u003c\/p\u003e \u003cp\u003e8.5 Multidimensional Search Without Using Derivatives.\u003c\/p\u003e \u003cp\u003e8.6 Multidimensional Search Using Derivatives.\u003c\/p\u003e \u003cp\u003e8.7 Modification of Newton's Method: Levenberg-Marquardt and Trust Region Methods.\u003c\/p\u003e \u003cp\u003e8.8 Methods Using Conjugate Directions: Quasi-Newton and Conjugate Gradient Methods.\u003c\/p\u003e \u003cp\u003e8.9 Subgradient Optimization Methods.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003eNotes and References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Penalty and Barrier Functions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Concept of Penalty Functions.\u003c\/p\u003e \u003cp\u003e9.2 Exterior Penalty Function Methods.\u003c\/p\u003e \u003cp\u003e9.3 Exact Absolute Value and Augmented Lagrangian Penalty Methods.\u003c\/p\u003e \u003cp\u003e9.4 Barrier Function Methods.\u003c\/p\u003e \u003cp\u003e9.5 Polynomial-Time Interior Point Algorithms for Linear Programming Based on a Barrier Function.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003eNotes and References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Methods of Feasible Directions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Method of Zoutendijk.\u003c\/p\u003e \u003cp\u003e10.2 Convergence Analysis of the Method of Zoutendijk.\u003c\/p\u003e \u003cp\u003e10.3 Successive Linear Programming Approach.\u003c\/p\u003e \u003cp\u003e10.4 Successive Quadratic Programming or Projected Lagrangian Approach.\u003c\/p\u003e \u003cp\u003e10.5 Gradient Projection Method of Rosen.\u003c\/p\u003e \u003cp\u003e10.6 Reduced Gradient Method of Wolfe and Generalized Reduced Gradient Method.\u003c\/p\u003e \u003cp\u003e10.7 Convex-Simplex Method of Zangwill.\u003c\/p\u003e \u003cp\u003e10.8 Effective First- and Second-Order Variants of the Reduced Gradient Method.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003eNotes and References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Linear Complementary Problem, and Quadratic, Separable, Fractional, and Geometric Programming.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Linear Complementary Problem.\u003c\/p\u003e \u003cp\u003e11.2 Convex and Nonconvex Quadratic Programming: Global Optimization Approaches.\u003c\/p\u003e \u003cp\u003e11.3 Separable Programming.\u003c\/p\u003e \u003cp\u003e11.4 Linear Fractional Programming.\u003c\/p\u003e \u003cp\u003e11.5 Geometric Programming.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003eNotes and References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Mathematical Review.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Summary of Convexity, Optimality Conditions, and Duality.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Interscience","offers":[{"title":"Brand 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