Mathematics of Nonlinear Programming - libro usado

EAN (ISBN-13): 9780387966144
ISBN (ISBN-10): 0387966145
Tapa dura
Tapa blanda
Año de publicación: 1993
Editorial: Springer
292 Páginas
Peso: 0,602 kg
Idioma: eng/Englisch

Libro en la base de datos desde 2008-01-13T10:19:30-06:00 (Mexico City)
Página de detalles modificada por última vez el 2024-01-20T15:54:02-06:00 (Mexico City)
ISBN/EAN: 0387966145

ISBN - escritura alterna:
0-387-96614-5, 978-0-387-96614-4
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: peress, peres, anthony, uhl, sullivan, lagrange
Título del libro: linear programming, the mathemati, the mathematics nonlinear programming, undergraduate text

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Datos del la editorial

Autor: Anthony L. Peressini; Francis E. Sullivan; J.J. Jr. Uhl
Título: Undergraduate Texts in Mathematics; The Mathematics of Nonlinear Programming
Editorial: Springer; Springer US
276 Páginas
Año de publicación: 1988-03-02
New York; NY; US
Idioma: Inglés
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.