This book provides an introduction to the mathematical theory of optimization. It emphasizes the convergence theory of nonlinear optimization algorithms and applications of nonlinear optimization to combinatorial optimization. Mathematical Theory of Optimization includes recent developments in global convergence, the Powell conjecture, semidefinite programming, and relaxation techniques for designs of approximation solutions of combinatorial optimization problems.
Mathematical Theory of Optimization
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Inside This Book
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First Sentence:
Accurate modeling of scientific problems often leads to the formulation of optimization problems involving continuous and/or discrete variables. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
sequence whose cluster points, slope lemma, reducible basis, ascendant direction, extremal basis, feasible basic solution, line search procedure, strictly feasible solution, reduced gradient method, exact line search, gradient projection method, variable metric methods, semidefinite programming, maximum clique problem, differentiable real function, feasible direction, feasible basis, convex envelope, nondegeneracy assumption, updating formula, feasible domain, basic feasible solution, global convergence, vertex cover, three given points Key Phrases - Capitalized Phrases (CAPs): (learn more)
Fritz John, Zangwill's Algorithm New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Accurate modeling of scientific problems often leads to the formulation of optimization problems involving continuous and/or discrete variables. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
sequence whose cluster points, slope lemma, reducible basis, ascendant direction, extremal basis, feasible basic solution, line search procedure, strictly feasible solution, reduced gradient method, exact line search, gradient projection method, variable metric methods, semidefinite programming, maximum clique problem, differentiable real function, feasible direction, feasible basis, convex envelope, nondegeneracy assumption, updating formula, feasible domain, basic feasible solution, global convergence, vertex cover, three given points Key Phrases - Capitalized Phrases (CAPs): (learn more)
Fritz John, Zangwill's Algorithm New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 22
books:
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9
books
cite this book:
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- Nonlinear Programming (Classics in Applied Mathematics) by Olvi L. Mangasarian in Back Matter (1), Back Matter (2), and Back Matter (3)
- Nonlinear Programming: Sequential Unconstrained Minimization Techniques (Classics in Applied Mathematics) by Anthony V. Fiacco in Back Matter (1), and Back Matter (2)
- Computational Methods in Optimization: A Unified Approach (Mathematics in Science and Engineering Ser. : Vol 77) by Author Unknown in Back Matter
- Satisfiability Problem: Theory and Applications (Dimacs Series in Discrete Mathematics and Theoretical Computer Science) by Jun Gu in Back Matter
- Constrained Global Optimization: Algorithms and Applications (Lecture Notes in Computer Science) by Panos M. Pardalos in Back Matter
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- Variational and Non-Variational Methods in Nonlinear Analysis and Boundary Value Problems (Nonconvex Optimization and Its Applications (closed)) by Dumitru Motreanu in Back Matter
- Nonsmooth Equations in Optimization: Regularity, Calculus, Methods and Applications (Nonconvex Optimization and Its Applications (closed)) by Diethard Klatte on page 333
- Multivalued Analysis and Nonlinear Programming Problems with Perturbations (Nonconvex Optimization and Its Applications (closed)) by B. Luderer in Back Matter
- Handbook of Global Optimization Volume 2 (Nonconvex Optimization and Its Applications (closed)) by Panos M. Pardalos in Back Matter
- Complementarity, Equilibrium, Efficiency and Economics (Nonconvex Optimization and Its Applications (closed)) by G. Isac in Back Matter
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