Convexity and Optimization in Rn
1st Edition
Published on 29. January 2002
Book
Hardback
280 pages
978-0-471-35281-5 (ISBN)
Description
A comprehensive introduction to convexity and optimization inRn
This book presents the mathematics of finite dimensionalconstrained optimization problems. It provides a basis for thefurther mathematical study of convexity, of more generaloptimization problems, and of numerical algorithms for the solutionof finite dimensional optimization problems. For readers who do nothave the requisite background in real analysis, the author providesa chapter covering this material. The text features abundantexercises and problems designed to lead the reader to a fundamentalunderstanding of the material.
Convexity and Optimization in Rn provides detailed discussionof:
* Requisite topics in real analysis
* Convex sets
* Convex functions
* Optimization problems
* Convex programming and duality
* The simplex method
A detailed bibliography is included for further study and an indexoffers quick reference. Suitable as a text for both graduate andundergraduate students in mathematics and engineering, thisaccessible text is written from extensively class-tested notes.
This book presents the mathematics of finite dimensionalconstrained optimization problems. It provides a basis for thefurther mathematical study of convexity, of more generaloptimization problems, and of numerical algorithms for the solutionof finite dimensional optimization problems. For readers who do nothave the requisite background in real analysis, the author providesa chapter covering this material. The text features abundantexercises and problems designed to lead the reader to a fundamentalunderstanding of the material.
Convexity and Optimization in Rn provides detailed discussionof:
* Requisite topics in real analysis
* Convex sets
* Convex functions
* Optimization problems
* Convex programming and duality
* The simplex method
A detailed bibliography is included for further study and an indexoffers quick reference. Suitable as a text for both graduate andundergraduate students in mathematics and engineering, thisaccessible text is written from extensively class-tested notes.
Reviews / Votes
"...a nice introduction to finite-dimensional optimization..."(Zentralblatt Math, Vol.991, No.16, 2002)"A textbook for a one-semester...course for students ofengineering, economics, operations research, and mathematics."(SciTech Book News, Vol. 26, No. 2, June 2002)
"...a fine introductory textbook that provides a solid introductionto the subject as well as a good foundation for further study..."(Mathematical Reviews, 2003a)
More details
Series
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Illustrations
Graphs: 21 B&W, 0 Color
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 20 mm
Weight
595 gr
ISBN-13
978-0-471-35281-5 (9780471352815)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Leonard D. Berkovitz
Convexity and Optimization in Rn
E-Book
03/2003
Wiley
€174.99
Available for download
Person
LEONARD D. BERKOVITZ, PhD, is Professor of Mathematics at Purdue University. He previously worked at the RAND Corporation and has served on the editorial boards of several journals, including terms as Managing Editor of the SIAM Journal on Control and as a member of the Editorial Committee of Mathematical Reviews.
Content
Preface.
I: Topics in Real Analysis.
1. Introduction.
2. Vectors in R".
3. Algebra of Sets.
4. Metric Topology of R".
5. Limits and Continuity.
6. Basic Propertyof Real Numbers.
7. Compactness.
8. Equivalent Norms and Cartesian Products.
9. Fundamental Existence Theorem.
10. Linear Transformations.
11. Differentiation in R".
II: Convex Sets in R".
1. Lines and Hyperplanes in R".
2. Properties of Convex Sets.
3. Separation Theorems.
4. Supporting Hyperplanes:Extreme Points.
5. Systems of Linear Inequalities:Theorems of the Alternative.
6. Affine Geometry.
7. More on Separation and Support.
III: Convex Functions.
1. Definition and Elementary Properties.
2. Subgradients.
3. Differentiable Convex Functions.
4. Alternative Theorems for Convex Functions.
5. Application to Game Theory.
IV: Optimization Problems.
1. Introduction.
2. Differentiable Unconstrained Problems.
3. Optimization of Convex Functions.
4. Linear Programming Problems.
5. First-Order Conditions for Differentiable NonlinearProgrammingProblems.
6. Second-Order Conditions.
V: Convex Programming and Duality.
1. Problem Statement.
2. Necessary Conditions and Sufficient Conditions.
3. Perturbation Theory.
4. Lagrangian Duality.
5. Geometric Interpretation.
6. Quadratic Programming.
7. Dualityin Linear Programming.
VI: Simplex Method.
1. Introduction.
2. Extreme Points of Feasible Set.
3. Preliminaries to Simplex Method.
4. Phase II of Simplex Method.
5. Termination and Cycling.
6. Phase I of Simplex Method.
7. Revised Simplex Method.
Bibliography.
Index.
I: Topics in Real Analysis.
1. Introduction.
2. Vectors in R".
3. Algebra of Sets.
4. Metric Topology of R".
5. Limits and Continuity.
6. Basic Propertyof Real Numbers.
7. Compactness.
8. Equivalent Norms and Cartesian Products.
9. Fundamental Existence Theorem.
10. Linear Transformations.
11. Differentiation in R".
II: Convex Sets in R".
1. Lines and Hyperplanes in R".
2. Properties of Convex Sets.
3. Separation Theorems.
4. Supporting Hyperplanes:Extreme Points.
5. Systems of Linear Inequalities:Theorems of the Alternative.
6. Affine Geometry.
7. More on Separation and Support.
III: Convex Functions.
1. Definition and Elementary Properties.
2. Subgradients.
3. Differentiable Convex Functions.
4. Alternative Theorems for Convex Functions.
5. Application to Game Theory.
IV: Optimization Problems.
1. Introduction.
2. Differentiable Unconstrained Problems.
3. Optimization of Convex Functions.
4. Linear Programming Problems.
5. First-Order Conditions for Differentiable NonlinearProgrammingProblems.
6. Second-Order Conditions.
V: Convex Programming and Duality.
1. Problem Statement.
2. Necessary Conditions and Sufficient Conditions.
3. Perturbation Theory.
4. Lagrangian Duality.
5. Geometric Interpretation.
6. Quadratic Programming.
7. Dualityin Linear Programming.
VI: Simplex Method.
1. Introduction.
2. Extreme Points of Feasible Set.
3. Preliminaries to Simplex Method.
4. Phase II of Simplex Method.
5. Termination and Cycling.
6. Phase I of Simplex Method.
7. Revised Simplex Method.
Bibliography.
Index.