Mathematics of Nonlinear Programming - livro usado

EAN (ISBN-13): 9780387966144
ISBN (ISBN-10): 0387966145
Livro de capa dura
Livro de bolso
Ano de publicação: 1993
Editor/Editora: Springer
292 Páginas
Peso: 0,602 kg
Língua: eng/Englisch

Livro na base de dados desde 2008-01-13T16:19:30+00:00 (Lisbon)
Página de detalhes modificada pela última vez em 2024-01-20T21:54:02+00:00 (Lisbon)
Número ISBN/EAN: 9780387966144

Número ISBN - Ortografia alternativa:
0-387-96614-5, 978-0-387-96614-4
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: peress, peres, anthony, uhl, sullivan, lagrange
Título do livro: linear programming, the mathemati, the mathematics nonlinear programming, undergraduate text

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Dados da editora

Autor: Anthony L. Peressini; Francis E. Sullivan; J.J. Jr. Uhl
Título: Undergraduate Texts in Mathematics; The Mathematics of Nonlinear Programming
Editora: Springer; Springer US
276 Páginas
Ano de publicação: 1988-03-02
New York; NY; US
Língua: Inglês
71,64 € (DE)
73,65 € (AT)
96,69 CHF (CH)
Available
X, 276 p.

BB; Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Numerische Mathematik; Verstehen; CON_D027; Computational Mathematics and Numerical Analysis; Quantitative Economics; Wirtschaftstheorie und -philosophie; BC

1 Unconstrained Optimization via Calculus.- 1.1. Functions of One Variable.- 1.2. Functions of Several Variables.- 1.3. Positive and Negative Definite Matrices and Optimization.- 1.4. Coercive Functions and Global Minimizers.- 1.5. Eigenvalues and Positive Definite Matrices.- Exercises.- 2 Convex Sets and Convex Functions.- 2.1. Convex Sets.- 2.2. Some Illustrations of Convex Sets in Economics— Linear Production Models.- 2.3. Convex Functions.- 2.4. Convexity and the Arithmetic-Geometric Mean Inequality— An Introduction to Geometric Programming.- 2.5. Unconstrained Geometric Programming.- 2.6. Convexity and Other Inequalities.- Exercises.- 3 Iterative Methods for Unconstrained Optimization.- 3.1. Newton’s Method.- 3.2. The Method of Steepest Descent.- 3.3. Beyond Steepest Descent.- 3.4. Broyden’s Method.- 3.5. Secant Methods for Minimization.- Exercises.- 4 Least Squares Optimization.- 4.1. Least Squares Fit.- 4.2. Subspaces and Projections.- 4.3. Minimum Norm Solutions of Underdetermined Linear Systems.- 4.4. Generalized Inner Products and Norms; The Portfolio Problem.- Exercises.- 5 Convex Programming and the Karush-Kuhn-Tucker Conditions.- 5.1. Separation and Support Theorems for Convex Sets.- 5.2. Convex Programming; The Karush-Kuhn-Tucker Theorem.- 5.3. The Karush-Kuhn-Tucker Theorem and Constrained Geometric Programming.- 5.4. Dual Convex Programs.- 5.5. Trust Regions.- Exercises.- 6 Penalty Methods.- 6.1. Penalty Functions.- 6.2. The Penalty Method.- 6.3. Applications of the Penalty Function Method to Convex Programs.- Exercises.- 7 Optimization with Equality Constraints.- 7.1. Surfaces and Their Tangent Planes.- 7.2. Lagrange Multipliers and the Karush-Kuhn-Tucker Theorem for Mixed Constraints.- 7.3. Quadratic Programming.- Exercises.