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E-grāmata: Fractals in Probability and Analysis

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This is a mathematically rigorous introduction to fractals which emphasizes examples and fundamental ideas. Building up from basic techniques of geometric measure theory and probability, central topics such as Hausdorff dimension, self-similar sets and Brownian motion are introduced, as are more specialized topics, including Kakeya sets, capacity, percolation on trees and the traveling salesman theorem. The broad range of techniques presented enables key ideas to be highlighted, without the distraction of excessive technicalities. The authors incorporate some novel proofs which are simpler than those available elsewhere. Where possible, chapters are designed to be read independently so the book can be used to teach a variety of courses, with the clear structure offering students an accessible route into the topic.

This book offers a mathematically rigorous introduction to fractals, emphasizing examples and fundamental ideas while minimizing technicalities. The clear presentation of a broad range of techniques makes the volume accessible to graduate students, while the independent nature of chapters renders it a useful supplement or resource for a variety of courses.

Recenzijas

'Fractal sets are now a key ingredient of much of mathematics, ranging from dynamical systems, transformation groups, stochastic processes, to modern analysis. This delightful book gives a correspondingly broad view of fractal sets. The presentation is original, clear and thoughtful, often with new and interesting approaches. It is suited both to graduate students and researchers, discussing reasonably easily accessible questions as well as research topics that are being actively investigated today. For example, in addition to learning about fractals, students will get new insights into some core topics, such as Brownian motion, while researchers will find new ideas for up-to-date research, for example related to analysts' traveling salesman problems. The book is splendid for a variety of graduate courses, most sections being essentially independent of each other, and is supported by a very large number of exercises of varying levels with hints and solutions.' Pertti Mattila, University of Helsinki 'This is a wonderful book, introducing the reader into the modern theory of fractals. It uses tools from analysis and probability very elegantly, and starting from the basics ends with a selection of deep and important results. The authors worked hard to achieve clarity; the book contains many original proofs which are expository gems. The book would serve very well for a graduate course; it is highly recommended both for students and for experts. A notable feature is a wide selection of exercises, some quite challenging, but made more accessible with an appendix containing selected hints.' Boris Solomyak, Bar-Ilan University, Israel 'This is a very valuable contribution to the field of geometric measure theory and its interactions with other branches of mathematical analysis and probability. The notions of Hausdorff measure, Hausdorff dimension and Minkowski dimension are central objects in this text, as in other books on geometric measure theory. What is special in this text, written by two major experts in geometric analysis and probability, is the emphasis on problems lying in the intersection of probability and analysis. In particular, the book studies a variety of questions in connection with self-similar sets, Frostman's theory, Weierstrass functions, Brownian motion and its relationship with the Dirichlet problem for harmonic functions, BesicovitchKakeya sets, and Jones' traveling salesman theorem. Many of the problems considered in the book are difficult to find in the literature. Further, very often their proofs are based on new and illuminating arguments. All in all, I think that this is a great book.' Xavier Tolsa, ICREA, Catalan Institution for Research and Advanced Studies, and Universitat Autņnoma de Barcelona 'This book, written by two of the best specialists in the world, is centered on the probabilistic aspects of geometric measure theory and fractals, but also contains beautiful pure analysis arguments. The point of view is very concrete, often based on many interesting examples or methods rather than a general theory. The most impressive aspect of the book is the huge collection of exercises of all levels, which will make a serious reading of the book both a pleasure and, if the reader wants to do them all, a performance.' Guy David, Université de Paris Sud 'There are at least two outstanding features of Bishop-Peres's new textbook that will help it stand with self-assurance The first feature is the remarkable clarity of exposition. The proofs are beautifully presented, with a stress on communicating ideas and methods (over technicalities). This leads the authors to study the simplest cases of problems/results that already contain the most important ideas. The second feature, which moves this text into its own class among existing graduate texts on the subject, is an exceptional list of 378 exercises.' Tushar Das, MAA Reviews 'This is a technical monograph suited to practioners of geometric measure theory and analysis written by two of the world's leaders in the field. It would make a serious study for graduate students, containing a large number of helpful examples.' Chris Athorne, Contemporary Physics

Papildus informācija

A mathematically rigorous introduction to fractals, emphasizing examples and fundamental ideas while minimizing technicalities.
Preface
1 Minkowski and Hausdorff dimensions
1(44)
1.1 Minkowski dimension
1(3)
1.2 Hausdorff dimension and the Mass Distribution Principle
4(5)
1.3 Sets denned by digit restrictions
9(8)
1.4 Billingsley's Lemma and the dimension of measures
17(4)
1.5 Sets defined by digit frequency
21(5)
1.6 Slices
26(3)
1.7 Intersecting translates of Cantor sets *
29(5)
1.8 Notes
34(2)
1.9 Exercises
36(9)
2 Self-similarity and packing dimension
45(38)
2.1 Self-similar sets
45(6)
2.2 The open set condition is sufficient
51(3)
2.3 Homogeneous sets
54(3)
2.4 Microsets
57(4)
2.5 Poincare sets *
61(6)
2.6 Alternative definitions of Minkowski dimension
67(4)
2.7 Packing measures and dimension
71(3)
2.8 When do packing and Minkowski dimension agree?
74(2)
2.9 Notes
76(2)
2.10 Exercises
78(5)
3 Frostman's theory and capacity
83(36)
3.1 Frostman's Lemma
83(5)
3.2 The dimension of product sets
88(2)
3.3 Generalized Marstrand Slicing Theorem
90(3)
3.4 Capacity and dimension
93(2)
3.5 Marstrand's Projection Theorem
95(5)
3.6 Mapping a tree to Euclidean space preserves capacity
100(4)
3.7 Dimension of random Cantor sets
104(8)
3.8 Notes
112(3)
3.9 Exercises
115(4)
4 Self-affine sets
119(17)
4.1 Construction and Minkowski dimension
119(2)
4.2 The Hausdorff dimension of self-affine sets
121(4)
4.3 A dichotomy for Hausdorff measure
125(2)
4.4 The Hausdorff measure is infinite *
127(4)
4.5 Notes
131(2)
4.6 Exercises
133(3)
5 Graphs of continuous functions
136(24)
5.1 Holder continuous functions
136(4)
5.2 The Weierstrass function is nowhere differentiable
140(5)
5.3 Lower Holder estimates
145(4)
5.4 Notes
149(2)
5.5 Exercises
151(9)
6 Brownian motion, Part I
160(41)
6.1 Gaussian random variables
160(3)
6.2 Levy's construction of Brownian motion
163(4)
6.3 Basic properties of Brownian motion
167(5)
6.4 Hausdorff dimension of the Brownian path and graph
172(4)
6.5 Nowhere differentiability is prevalent
176(2)
6.6 Strong Markov property and the reflection principle
178(2)
6.7 Local extrema of Brownian motion
180(1)
6.8 Area of planar Brownian motion
181(2)
6.9 General Markov processes
183(2)
6.10 Zeros of Brownian motion
185(4)
6.11 Harris' inequality and its consequences
189(3)
6.12 Points of increase
192(4)
6.13 Notes
196(3)
6.14 Exercises
199(2)
7 Brownian motion, Part II
201(43)
7.1 Dimension doubling
201(5)
7.2 The Law of the Iterated Logarithm
206(3)
7.3 Skorokhod's Representation
209(7)
7.4 Donsker's Invariance Principle
216(5)
7.5 Harmonic functions and Brownian motion in Rd
221(5)
7.6 The maximum principle for harmonic functions
226(1)
7.7 The Dirichlet problem
227(1)
7.8 Polar points and recurrence
228(2)
7.9 Conformal invariance *
230(5)
7.10 Capacity and harmonic functions
235(4)
7.11 Notes
239(2)
7.12 Exercises
241(3)
8 Random walks, Markov chains and capacity
244(26)
8.1 Frostman's theory for discrete sets
244(6)
8.2 Markov chains and capacity
250(4)
8.3 Intersection equivalence and return times
254(4)
8.4 Lyons' Theorem on percolation on trees
258(2)
8.5 Dimension of random Cantor sets (again)
260(4)
8.6 Brownian motion and Martin capacity
264(2)
8.7 Notes
266(1)
8.8 Exercises
266(4)
9 Besicovitch--Kakeya sets
270(43)
9.1 Existence and dimension
270(6)
9.2 Splitting triangles
276(2)
9.3 Fefferman's Disk Multiplier Theorem *
278(8)
9.4 Random Besicovitch sets
286(4)
9.5 Projections of self-similar Cantor sets
290(7)
9.6 The open set condition is necessary *
297(5)
9.7 Notes
302(3)
9.8 Exercises
305(8)
10 The Traveling Salesman Theorem
313(30)
10.1 Lines and length
313(5)
10.2 The β-numbers
318(4)
10.3 Counting with dyadic squares
322(3)
10.4 β and μ are equivalent
325(4)
10.5 β-sums estimate minimal paths
329(5)
10.6 Notes
334(3)
10.7 Exercises
337(6)
Appendix A Banach's Fixed-Point Theorem 343(10)
Appendix B Frostman's Lemma for analytic sets 353(7)
Appendix C Hints and solutions to selected exercises 360(19)
References 379(17)
Index 396
Christopher J. Bishop is a professor in the Department of Mathematics at Stony Brook University, New York. He has made contributions to the theory of function algebras, Kleinian groups, harmonic measure, conformal and quasiconformal mapping, holomorphic dynamics and computational geometry. Yuval Peres is a Principal Researcher at Microsoft Research in Redmond, Washington. He is particularly known for his research in topics such as fractals and Hausdorff measures, random walks, Brownian motion, percolation and Markov chain mixing times.