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Mathematics of Nonlinear Programming - Anthony L. Peressini, Francis E. Sullivan and J.J. Jr. Uhl
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ISBN: 9780387966144

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Mathematics of Nonlinear Programming - Anthony L. Peressini, Francis E. Sullivan and J.J. Jr. Uhl
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Anthony L. Peressini, Francis E. Sullivan and J.J. Jr. Uhl:

Mathematics of Nonlinear Programming - used book

ISBN: 9780387966144

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The Mathematics of Nonlinear Programming - Anthony L. Peressini
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Anthony L. Peressini:
The Mathematics of Nonlinear Programming - hardcover

1988

ISBN: 0387966145

[EAN: 9780387966144], Neubuch, [PU: Springer-Verlag New York Inc.], This item is printed on demand. New copy - Usually dispatched within 5-9 working days, Books

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The Mathematics of Nonlinear Programming - Peressini, Anthony L.
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The Mathematics of Nonlinear Programming - used book

ISBN: 9780387966144

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Peressini, Anthony L.:
The Mathematics of Nonlinear Programming - new book

ISBN: 9780387966144

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Details of the book
The Mathematics of Nonlinear Programming (Undergraduate Texts in Mathematics)

Nonlinear programming provides an excellent opportunity to explore an interesting variety of pure and solidly applicable mathematics, numerical analysis, and computing. This text develops some of the ideas and techniques involved in the optimization methods using calculus, leading to the study of convexity. This is followed by material on basic numerical methods, least squares, the Karush-Kuhn-Tucker theorem, penalty functions, and Lagrange multipliers. The authors have aimed their presentation at the student who has a working knowledge of matrix algebra and advanced calculus, but has had no previous exposure to optimization. TOC:BContents:/B Unconstrained Optimization via Calculus.- Convex Sets and Convex Functions.- Iterative Methods for Unconstrained Optimization.- Least Squares Optimization.- Convex Programming and the Karush-Kuhn-Tucker Conditions.- Penalty Methods.- Optimization with Equality Constraints.- Index.

Details of the book - The Mathematics of Nonlinear Programming (Undergraduate Texts in Mathematics)


EAN (ISBN-13): 9780387966144
ISBN (ISBN-10): 0387966145
Hardcover
Paperback
Publishing year: 1993
Publisher: Springer
292 Pages
Weight: 0,602 kg
Language: eng/Englisch

Book in our database since 2008-01-13T11:19:30-05:00 (New York)
Detail page last modified on 2024-01-20T16:54:02-05:00 (New York)
ISBN/EAN: 0387966145

ISBN - alternate spelling:
0-387-96614-5, 978-0-387-96614-4
Alternate spelling and related search-keywords:
Book author: peress, peres, anthony, uhl, sullivan, lagrange
Book title: linear programming, the mathemati, the mathematics nonlinear programming, undergraduate text


Information from Publisher

Author: Anthony L. Peressini; Francis E. Sullivan; J.J. Jr. Uhl
Title: Undergraduate Texts in Mathematics; The Mathematics of Nonlinear Programming
Publisher: Springer; Springer US
276 Pages
Publishing year: 1988-03-02
New York; NY; US
Language: English
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.

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