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E-raamat: Course in Convexity

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Convexity is a simple idea that manifests itself in a surprising variety of places. This fertile field has an immensely rich structure and numerous applications. Barvinok demonstrates that simplicity, intuitive appeal, and the universality of applications make teaching (and learning) convexity a gratifying experience. The book will benefit both teacher and student: It is easy to understand, entertaining to the reader, and includes many exercises that vary in degree of difficulty. Overall, the author demonstrates the power of a few simple unifying principles in a variety of pure and applied problems. The notion of convexity comes from geometry. Barvinok describes here its geometric aspects, yet he focuses on applications of convexity rather than on convexity for its own sake. Mathematical applications range from analysis and probability to algebra to combinatorics to number theory.Several important areas are covered, including topological vector spaces, linear programming, ellipsoids, and lattices. Specific topics of note are optimal control, sphere packings, rational approximations, numerical integration, graph theory, and more. And of course, there is much to say about applying convexity theory to the study of faces of polytopes, lattices and polyhedra, and lattices and convex bodies. The prerequisites are minimal amounts of linear algebra, analysis, and elementary topology, plus basic computer skills. Portions of the book could be used by advanced undergraduates. As a whole, it is designed for graduate students interested in mathematical methods, computer science, electrical engineering, and operations research. Readers will find some new results. Also, many known results are discussed from a new perspective.
Preface vii
Convex Sets at Large
1(40)
Convex Sets. Main Definitions, Some Interesting Examples and Problems
1(6)
Properties of the Convex Hull. Caratheodory's Theorem
7(5)
An Application: Positive Polynomials
12(5)
Theorems of Radon and Helly
17(4)
Applications of Helly's Theorem in Combinatorial Geometry
21(3)
An Application to Approximation
24(4)
The Euler Characteristic
28(5)
Application: Convex Sets and Linear Transformations
33(4)
Polyhedra and Linear Transformations
37(2)
Remarks
39(2)
Faces and Extreme Points
41(64)
The Isolation Theorem
41(6)
Convex Sets in Euclidean Space
47(4)
Extreme Points. The Krein-Milman Theorem for Euclidean Space
51(2)
Extreme Points of Polyhedra
53(3)
The Birkhoff Polytope
56(2)
The Permutation Polytope and the Schur-Horn Theorem
58(2)
The Transportation Polyhedron
60(5)
Convex Cones
65(2)
The Moment Curve and the Moment Cone
67(3)
An Application: ``Double Precision'' Formulas for Numerical Integration
70(3)
The Cone of Non-negative Polynomials
73(5)
The Cone of Positive Semidefinite Matrices
78(5)
Linear Equations in Positive Semidefinite Matrices
83(6)
Applications: Quadratic Convexity Theorems
89(5)
Applications: Problems of Graph Realizability
94(5)
Closed Convex Sets
99(4)
Remarks
103(2)
Convex Sets in Topological Vector Spaces
105(38)
Separation Theorems in Euclidean Space and Beyond
105(4)
Topological Vector Spaces, Convex Sets and Hyperplanes
109(8)
Separation Theorems in Topological Vector Spaces
117(4)
The Krein-Milman Theorem for Topological Vector Spaces
121(2)
Polyhedra in L∞
123(3)
An Application: Problems of Linear Optimal Control
126(4)
An Application: The Lyapunov Convexity Theorem
130(3)
The ``Simplex'' of Probability Measures
133(3)
Extreme Points of the Intersection. Applications
136(5)
Remarks
141(2)
Polarity, Duality and Linear Programming
143(60)
Polarity in Euclidean Space
143(7)
An Application: Recognizing Points in the Moment Cone
150(4)
Duality of Vector Spaces
154(3)
Duality of Topological Vector Spaces
157(3)
Ordering a Vector Space by a Cone
160(2)
Linear Programming Problems
162(4)
Zero Duality Gap
166(6)
Polyhedral Linear Programming
172(4)
An Application: The Transportation Problem
176(2)
Semidefinite Programming
178(4)
An Application: The Clique and Chromatic Numbers of a Graph
182(3)
Linear Programming in L∞
185(6)
Uniform Approximation as a Linear Programming Problem
191(5)
The Mass-Transfer Problem
196(6)
Remarks
202(1)
Convex Bodies and Ellipsoids
203(46)
Ellipsoids
203(4)
The Maximum Volume Ellipsoid of a Convex Body
207(9)
Norms and Their Approximations
216(9)
The Ellipsoid Method
225(7)
The Gaussian Measure on Euclidean Space
232(8)
Applications to Low Rank Approximations of Matrices
240(4)
The Measure and Metric on the Unit Sphere
244(4)
Remarks
248(1)
Faces of Polytopes
249(30)
Polytopes and Polarity
249(5)
The Facial Structure of the Permutation Polytope
254(4)
The Euler-Poincare Formula
258(4)
Polytopes with Many Faces: Cyclic Polytopes
262(2)
Simple Polytopes
264(3)
The h-vector of a Simple Polytope. Dehn-Sommerville Equations
267(3)
The Upper Bound Theorem
270(4)
Centrally Symmetric Polytopes
274(3)
Remarks
277(2)
Lattices and Convex Bodies
279(46)
Lattices
279(7)
The Determinant of a Lattice
286(7)
Minkowski's Convex Body Theorem
293(5)
Applications: Sums of Squares and Rational Approximations
298(4)
Sphere Packings
302(3)
The Minkowski-Hlawka Theorem
305(4)
The Dual Lattice
309(6)
The Flatness Theorem
315(4)
Constructing a Short Vector and a Reduced Basis
319(5)
Remarks
324(1)
Lattice Points and Polyhedra
325(32)
Generating Functions and Simple Rational Cones
325(5)
Generating Functions and Rational Cones
330(5)
Generating Functions and Rational Polyhedra
335(6)
Brion's Theorem
341(8)
The Ehrhart Polynomial of a Polytope
349(4)
Example: Totally Unimodular Polytopes
353(3)
Remarks
356(1)
Bibliography 357(6)
Index 363