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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Nash , Ariela Sofer Published Mathematics. Preface Part I. Basics: 1. Optimization models 2. Fundamentals of optimization 3. Representation of linear constraints Part II. Linear Programming: 4. Geometry of linear programming 5. The simplex method 6. Duality and sensitivity 7.

Enhancements of the simplex method 8. Network problems 9. Computational complexity of linear programming Interior-point methods of linear programming Part III. Unconstrained Optimization: Basics of unconstrained optimization View PDF. Save to Library. Create Alert. Launch Research Feed. Share This Paper. Citations Publications citing this paper.

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Siong Tok Engineering, Computer Science Kropatsch , Nicole M. References Publications referenced by this paper. Jean Abadie Mathematics Abadie , J. Theoretical foundation of the potential function method in pattern recognition learning. Aizerman , E. Braverman , L. Rozonoer Automation and Remote Control, 25 Anstreicher , Robert A.

Optimization Presolving in linear programming Erling D. Andersen , Knud D. Andersen Mathematics, Computer Science Math. The expected number of pivots needed to solve parametric linear programs and the efficiency of the s I. Adler Mathematics Kuhn Computer Science Related Papers.

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Linear and Nonlinear Optimization

Linear and Nonlinear Optimization : Second Edition. Igor Griva , Stephen G. Nash , Ariela Sofer. Provides an introduction to the applications, theory, and algorithms of linear and nonlinear optimization. The emphasis is on practical aspects - discussing modern algorithms, as well as the influence of theory on the interpretation of solutions or on the design of software. The book includes several examples of realistic optimization models that address important applications. The succinct style of this second edition is punctuated with numerous real-life examples and exercises, and the authors include accessible explanations of topics that are not often mentioned in textbooks, such as duality in nonlinear optimization, primal-dual methods for nonlinear optimization, filter methods, and applications such as support-vector machines.


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