Natural Proof of the Characterization of Relatively Compact Families in Lp

Total Page:16

File Type:pdf, Size:1020Kb

Natural Proof of the Characterization of Relatively Compact Families in Lp Natural proof of the characterization of relatively compact families in Lp−spaces on locally compact groups Mateusz Krukowski Institute of Mathematics, Łódź University of Technology, Wólczańska 215, 90-924 Łódź, Poland e-mail: [email protected] July 13, 2020 Abstract In the paper we look for an elegant proof of the characterization of relatively compact families in Lp−spaces. At first glance, the suggested approach may seem convoluted and lengthy, but we spare no effort to argue that our proof is in fact more natural than the ones existent in the litera- ture. The key idea is that the three properties which characterize relative compactness in Lp−spaces (Lp−boundedness, Lp−equicontinuity and Lp−equivanishing) are “preserved” (or rather “inherited”) when the family F ⊂ Lp is convolved with a continuous and compactly supported function. This new family turns out to be relatively compact in C0−space and it remains to be demonstrated that relative compactness in C0−space implies relative compactness of the original family F. Keywords : Arzelà-Ascoli theorem, Kolmogorov-Riesz theorem, Weil theorem, Sudakov theorem, Young’s convolution inequality AMS Mathematics Subject Classification (2010) : 43A15, 46B50, 46E15, 46E30 1 Introduction Let us begin with a brief historical overview1: Andrey Kolmogorov (1903 − 1987) was arguably the arXiv:1801.01898v3 [math.FA] 10 Jul 2020 first person, who succeeded in characterizing relatively compact families in Lp(RN ), when 1 <p< ∞ and all functions are supported in a common bounded set2. A year later (in 1932) Jacob David Tamarkin (1888 − 1945) got rid of the second restriction3 and in 1933 Marcel Riesz (1886 − 1969), a younger brother of Frigyes Riesz (1880 − 1956), proved the general case4: Theorem 1. Let 1 6 p< ∞. A family F ⊂ Lp RN is relatively compact if and only if 1See [20] for a much more detailed account. 2See [26] for the original paper by Kolmogorov. 3See [38] for the original paper by Tamarkin. 4See [32] for the original paper by Riesz. 1 • F is Lp−bounded, i.e. there exists M > 0 such that ∀f∈F kfkp 6 M, • F is Lp−equicontinuous, i.e. for every ε> 0 there exists δ > 0 such that p ∀|y|<δ |f(x + y) − f(x)| dx 6 ε, RN f∈F Z • F is Lp−equivanishing, i.e. for every ε> 0 there exists R> 0 such that p ∀f∈F |f(x)| dx 6 ε. Z|x|>R In 1940, André Weil (1906 − 1998) published a book “L’intégration dans les groupes topologique”, in which he demonstrated that Theorem 1 holds true even if we replace RN with a locally compact Hausdorff group G5: Theorem 2. Let G be a locally compact Hausdorff group. A family F ⊂ Lp(G) is relatively compact if and only if • F is Lp−bounded, p • F is L −equicontinuous, i.e. for every ε > 0 there exists an open neighbourhood Ue of the neutral element e such that p ∀y∈Ue |f(xy) − f(x)| dx 6 ε, f∈F ZG • F is Lp−equivanishing, i.e. for every ε > 0 there exists K ⋐ G (which means that K is a compact subset of G) such that p ∀f∈F |f(x)| dx 6 ε. ZG\K Weil’s book is oftentimes cited as a reference source when it comes to characterizing relatively compact families in Lp−spaces6. However, the argument of the french mathematician is (at least in the author’s opinion) rather difficult to follow − the exposition is very terse, avoids technical details and leaves much of the work to the reader. The fact that the book is written in French does not make matters easier. Our goal is to overcome these inconveniences. As far as the organization of the paper is concerned, Chapter 2 is devoted to the Arzelà-Ascoli theorem. As we document, the classic version of this theorem (for C(X), X being a compact space) is very well- established in the literature, but the version for C0(X), X being a locally compact space, is almost non-existent. In our approach we employ the concept of one-point compactification X∞ of X. Following a brief description of this construct we lay out a full proof of the Arzelà-Ascoli theorem for C0(X). Chapter 3 commences with a short summary of the Haar measure. Subsequently, we introduce three Lp−properties: Lp−boundedness, Lp−equicontinuity and Lp−equivanishing. Next, Theorems 6, 7 and 5See [39], p. 53-54 for Weil’s argument (in french). 6See [11, 17, 18, 19, 20]. 2 9 demonstrate how these Lp−properties are “passed down” from F ⊂ Lp(G), G being a locally compact group, to F ⋆ φ ⊂ C0(G), where φ ∈ Cc(G). Chapter 4 is divided into two parts: the first one pivots around proving that F ⋆ φ is “not far away” (if we choose φ ∈ Cc(G) properly) from F (see Theorem 11). The subject of the second part is Young’s convolution inequality, which we generalize beyond what is currently known in the literature. Finally, Chapter 5 brings all the “pieces of the puzzle” from previous chapters together. In the climax of the paper (see Theorem 14) we provide an elegant and natural proof of the characterization of the relatively compact families in Lp(G). 2 Arzelà-Ascoli theorem and one-point compactification The classic version of the Arzelà-Ascoli theorem characterizes relatively compact families in C(X), the space of continuous functions on a compact space X (we assume that every topological space in this paper is Hausdorff). Such version of the result appears in many sources throughout the literature7, although some authors assume additionally that the space X is metric for “simplicity of the proof”8. Theorem 3. (classic version of the Arzelà-Ascoli theorem) Let X be a compact space. The family F ⊂ C(X) is relatively compact if and only if • F is pointwise bounded, i.e. for every x ∈ X there exists Mx > 0 such that ∀f∈F |f(x)| 6 Mx, • F is equicontinuous at every point, i.e. for every x ∈ X and ε> 0 there exists an open neighbourhood Ux of x such that ∀y∈Ux, |f(y) − f(x)| 6 ε. f∈F For our purposes, we need a more general result. We need a characterization of relatively compact families in C0(X), the space of continuous functions on a locally compact space X which vanish at infinity, i.e. for every ε> 0 there exists a compact set K in X (we denote this situation by K ⋐ X) such that ∀x∈X\K |f(x)| 6 ε. Looking through the literature, one usually stumbles across versions of the Arzelà-Ascoli theorem in which the supremum-norm topology is replaced with the compact-open topology9. Theorem 4. (Arzelà-Ascoli theorem for C0(X) with the compact-open topology) Let X be a locally compact space. The family F ⊂ C0(X) is relatively compact in the compact-open topology if and only if • F is pointwise bounded, • F is equicontinuous at every point. 7See Theorem 4.43 in [13], p. 137 or Corollary 10.49 in [25], p. 479 or Theorem A5 in [34], p. 394. 8See Theorem 4.25 in [2], p. 111 or Theorem 23.2 in [4], p. 99 or Theorem 6.3.1 in [9], p. 69 or Theorem 11.28 in [33], p. 245. 9See Theorem 3.4.20 in [10], p. 163 or Theorem 17 in [24], p. 233 or Theorem 47.1 in [27], p. 290. 3 The above result seems promising (to say the least), but it has a “splinter” in the form of the switch of topologies. Is there a version of the Arzelà-Ascoli theorem for C0(X) in which the supremum-norm topology is not replaced with compact-open topology (or any other topology for that matter)? The literature is surprisingly scarce in this respect. One possible reference is Exercise 17 on page 182 in John B. Conway’s “A Course in Functional Analysis”10. However, Conway does not provide a solution to his exercise, leaving this task to the reader. Another reference is the monograph “Integral Systems and Stability of Feedback Systems” by Constantin Corduneanu11. Unlike Conway, Corduneanu does provide a proof, but (in the author’s opinion) he does not provide an insight into why the theorem is true12. We aim to rectify this imperfection. For any locally compact space X, there exists a compact space X∞ such that • X is a subspace of X∞, • the closure of X is X∞, • X∞\X is a singleton or an empty set. 13 The space X∞ is called the one-point compactification of X . Its topology allows for a simple description − it consists of open sets in X (the topology of X) plus all sets of the form X∞\K, where K ⋐ X. If X happens to be compact itself, then X = X∞. Otherwise, X∞\X is a singleton, whose element is often denoted by ∞. A common example of the one-point compactification is C∞, which arises in the field of complex analysis as a natural domain for Möbius transformations. It turns out that C∞ is homeomorphic to a sphere S2, called the Riemann sphere, and the element ∞ may be regarded as the “north pole” of that sphere14. Theorem 5. (Arzelà-Ascoli theorem for C0(X) with the supremum-norm topology) Let X be a locally compact space. The family F ⊂ C0(X) is relatively compact in the supremum-norm topology if and only if • F is pointwise bounded, • F is equicontinuous at every point, • F is equivanishing, i.e.
Recommended publications
  • “To Be a Good Mathematician, You Need Inner Voice” ”Огонёкъ” Met with Yakov Sinai, One of the World’S Most Renowned Mathematicians
    “To be a good mathematician, you need inner voice” ”ОгонёкЪ” met with Yakov Sinai, one of the world’s most renowned mathematicians. As the new year begins, Russia intensifies the preparations for the International Congress of Mathematicians (ICM 2022), the main mathematical event of the near future. 1966 was the last time we welcomed the crème de la crème of mathematics in Moscow. “Огонёк” met with Yakov Sinai, one of the world’s top mathematicians who spoke at ICM more than once, and found out what he thinks about order and chaos in the modern world.1 Committed to science. Yakov Sinai's close-up. One of the most eminent mathematicians of our time, Yakov Sinai has spent most of his life studying order and chaos, a research endeavor at the junction of probability theory, dynamical systems theory, and mathematical physics. Born into a family of Moscow scientists on September 21, 1935, Yakov Sinai graduated from the department of Mechanics and Mathematics (‘Mekhmat’) of Moscow State University in 1957. Andrey Kolmogorov’s most famous student, Sinai contributed to a wealth of mathematical discoveries that bear his and his teacher’s names, such as Kolmogorov-Sinai entropy, Sinai’s billiards, Sinai’s random walk, and more. From 1998 to 2002, he chaired the Fields Committee which awards one of the world’s most prestigious medals every four years. Yakov Sinai is a winner of nearly all coveted mathematical distinctions: the Abel Prize (an equivalent of the Nobel Prize for mathematicians), the Kolmogorov Medal, the Moscow Mathematical Society Award, the Boltzmann Medal, the Dirac Medal, the Dannie Heineman Prize, the Wolf Prize, the Jürgen Moser Prize, the Henri Poincaré Prize, and more.
    [Show full text]
  • Excerpts from Kolmogorov's Diary
    Asia Pacific Mathematics Newsletter Excerpts from Kolmogorov’s Diary Translated by Fedor Duzhin Introduction At the age of 40, Andrey Nikolaevich Kolmogorov (1903–1987) began a diary. He wrote on the title page: “Dedicated to myself when I turn 80 with the wish of retaining enough sense by then, at least to be able to understand the notes of this 40-year-old self and to judge them sympathetically but strictly”. The notes from 1943–1945 were published in Russian in 2003 on the 100th anniversary of the birth of Kolmogorov as part of the opus Kolmogorov — a three-volume collection of articles about him. The following are translations of a few selected records from the Kolmogorov’s diary (Volume 3, pages 27, 28, 36, 95). Sunday, 1 August 1943. New Moon 6:30 am. It is a little misty and yet a sunny morning. Pusya1 and Oleg2 have gone swimming while I stay home being not very well (though my condition is improving). Anya3 has to work today, so she will not come. I’m feeling annoyed and ill at ease because of that (for the second time our Sunday “readings” will be conducted without Anya). Why begin this notebook now? There are two reasonable explanations: 1) I have long been attracted to the idea of a diary as a disciplining force. To write down what has been done and what changes are needed in one’s life and to control their implementation is by no means a new idea, but it’s equally relevant whether one is 16 or 40 years old.
    [Show full text]
  • Randomness and Computation
    Randomness and Computation Rod Downey School of Mathematics and Statistics, Victoria University, PO Box 600, Wellington, New Zealand [email protected] Abstract This article examines work seeking to understand randomness using com- putational tools. The focus here will be how these studies interact with classical mathematics, and progress in the recent decade. A few representa- tive and easier proofs are given, but mainly we will refer to the literature. The article could be seen as a companion to, as well as focusing on develop- ments since, the paper “Calibrating Randomness” from 2006 which focused more on how randomness calibrations correlated to computational ones. 1 Introduction The great Russian mathematician Andrey Kolmogorov appears several times in this paper, First, around 1930, Kolmogorov and others founded the theory of probability, basing it on measure theory. Kolmogorov’s foundation does not seek to give any meaning to the notion of an individual object, such as a single real number or binary string, being random, but rather studies the expected values of random variables. As we learn at school, all strings of length n have the same probability of 2−n for a fair coin. A set consisting of a single real has probability zero. Thus there is no meaning we can ascribe to randomness of a single object. Yet we have a persistent intuition that certain strings of coin tosses are less random than others. The goal of the theory of algorithmic randomness is to give meaning to randomness content for individual objects. Quite aside from the intrinsic mathematical interest, the utility of this theory is that using such objects instead distributions might be significantly simpler and perhaps giving alternative insight into what randomness might mean in mathematics, and perhaps in nature.
    [Show full text]
  • Fundamental Theorems in Mathematics
    SOME FUNDAMENTAL THEOREMS IN MATHEMATICS OLIVER KNILL Abstract. An expository hitchhikers guide to some theorems in mathematics. Criteria for the current list of 243 theorems are whether the result can be formulated elegantly, whether it is beautiful or useful and whether it could serve as a guide [6] without leading to panic. The order is not a ranking but ordered along a time-line when things were writ- ten down. Since [556] stated “a mathematical theorem only becomes beautiful if presented as a crown jewel within a context" we try sometimes to give some context. Of course, any such list of theorems is a matter of personal preferences, taste and limitations. The num- ber of theorems is arbitrary, the initial obvious goal was 42 but that number got eventually surpassed as it is hard to stop, once started. As a compensation, there are 42 “tweetable" theorems with included proofs. More comments on the choice of the theorems is included in an epilogue. For literature on general mathematics, see [193, 189, 29, 235, 254, 619, 412, 138], for history [217, 625, 376, 73, 46, 208, 379, 365, 690, 113, 618, 79, 259, 341], for popular, beautiful or elegant things [12, 529, 201, 182, 17, 672, 673, 44, 204, 190, 245, 446, 616, 303, 201, 2, 127, 146, 128, 502, 261, 172]. For comprehensive overviews in large parts of math- ematics, [74, 165, 166, 51, 593] or predictions on developments [47]. For reflections about mathematics in general [145, 455, 45, 306, 439, 99, 561]. Encyclopedic source examples are [188, 705, 670, 102, 192, 152, 221, 191, 111, 635].
    [Show full text]
  • A Modern History of Probability Theory
    A Modern History of Probability Theory Kevin H. Knuth Depts. of Physics and Informatics University at Albany (SUNY) Albany NY USA 4/29/2016 Knuth - Bayes Forum 1 A Modern History of Probability Theory Kevin H. Knuth Depts. of Physics and Informatics University at Albany (SUNY) Albany NY USA 4/29/2016 Knuth - Bayes Forum 2 A Long History The History of Probability Theory, Anthony J.M. Garrett MaxEnt 1997, pp. 223-238. Hájek, Alan, "Interpretations of Probability", The Stanford Encyclopedia of Philosophy (Winter 2012 Edition), Edward N. Zalta (ed.), URL = <http://plato.stanford.edu/archives/win2012/entries/probability-interpret/>. 4/29/2016 Knuth - Bayes Forum 3 … la théorie des probabilités n'est, au fond, que le bon sens réduit au calcul … … the theory of probabilities is basically just common sense reduced to calculation … Pierre Simon de Laplace Théorie Analytique des Probabilités 4/29/2016 Knuth - Bayes Forum 4 Taken from Harold Jeffreys “Theory of Probability” 4/29/2016 Knuth - Bayes Forum 5 The terms certain and probable describe the various degrees of rational belief about a proposition which different amounts of knowledge authorise us to entertain. All propositions are true or false, but the knowledge we have of them depends on our circumstances; and while it is often convenient to speak of propositions as certain or probable, this expresses strictly a relationship in which they stand to a corpus of knowledge, actual or hypothetical, and not a characteristic of the propositions in themselves. A proposition is capable at the same time of varying degrees of this relationship, depending upon the knowledge to which it is related, so that it is without significance to call a John Maynard Keynes proposition probable unless we specify the knowledge to which we are relating it.
    [Show full text]
  • Biography of N. N. Luzin
    BIOGRAPHY OF N. N. LUZIN http://theor.jinr.ru/~kuzemsky/Luzinbio.html BIOGRAPHY OF N. N. LUZIN (1883-1950) born December 09, 1883, Irkutsk, Russia. died January 28, 1950, Moscow, Russia. Biographic Data of N. N. Luzin: Nikolai Nikolaevich Luzin (also spelled Lusin; Russian: НиколайНиколаевич Лузин) was a Soviet/Russian mathematician known for his work in descriptive set theory and aspects of mathematical analysis with strong connections to point-set topology. He was the co-founder of "Luzitania" (together with professor Dimitrii Egorov), a close group of young Moscow mathematicians of the first half of the 1920s. This group consisted of the higly talented and enthusiastic members which form later the core of the famous Moscow school of mathematics. They adopted his set-theoretic orientation, and went on to apply it in other areas of mathematics. Luzin started studying mathematics in 1901 at Moscow University, where his advisor was professor Dimitrii Egorov (1869-1931). Professor Dimitrii Fedorovihch Egorov was a great scientist and talented teacher; in addition he was a person of very high moral principles. He was a Russian and Soviet mathematician known for significant contributions to the areas of differential geometry and mathematical analysis. Egorov was devoted and openly practicized member of Russian Orthodox Church and active parish worker. This activity was the reason of his conflicts with Soviet authorities after 1917 and finally led him to arrest and exile to Kazan (1930) where he died from heavy cancer. From 1910 to 1914 Luzin studied at Gottingen, where he was influenced by Edmund Landau. He then returned to Moscow and received his Ph.D.
    [Show full text]
  • Inner Simplicity Vs. Outer Simplicity
    Inner simplicity vs. outer simplicity Etienne´ Ghys Editors' note: The text is an edited transcript of the author's conference talk. For me, mathematics is just about understanding. And understanding is a personal and private feeling. However, to appreciate and express this feeling, you need to communicate with others|you need to use language. So there are necessarily two aspects in mathematics: one is very personal, emotional and internal, and the other is more public and external. Today I want to express this very na¨ıve idea for mathematicians that we should dis- tinguish between two kinds of simplicities. Something could be very simple for me, in my mind, and in my way of knowing mathematics, and yet be very difficult to articulate or write down in a mathematical paper. And con- versely, something can be very easy to write down or say in just one sentence of English or French or whatever, and nevertheless be all but completely in- accessible to my mind. This basic distinction is something which I believe to be classical but, nevertheless, we mathematicians conflate the two. We keep forgetting that writing mathematics is not the same as understanding mathematics. Let me begin with a memory that I have from when I was a student a long time ago. I was reading a book by a very famous French mathematician, Jean-Pierre Serre. Here is the cover of the book (Figure 1). For many years I was convinced that the title of the book was a joke. How else, I wondered, can these algebras be complex and simple at the same time? For mathematicians, of course, the words `complex' and `semisimple' have totally different meanings than their everyday ones.
    [Show full text]
  • Randomized Algorithm
    Randomized Algorithm Tutorial I Sep. 30 2008 Your TA: Abner C.Y. Huang [email protected] Think about Randomness Determinism vs. Randomness 2 Randomness in Real World 3 Joseph Louis Lagrange's deterministic universe ● offered the most comprehensive treatment of classical mechanics since Newton and formed a basis for the development of mathematical physics in the nineteenth century. 4 ● Werner Heisenberg ● Albert Einstein Heinsberg's Uncertainty God doesn't play dice with the Principle universe. 5 Randomness in Math. 6 Randomness in Mathematics ● Pascal was a mathematician of the first order. He helped create major new area of research, probability theory, with Pierre de Fermat. 7 Pascal's triangle 8 Source of Probability theory: Gambling 9 John Maynard Keynes The long run is a misleading guide to current affairs. In the long run we are all dead. 10 ● He got the Ph.D degree of mathematics at King’s College, Cambridge. ● His thesis is about logic in probabilistic viewpoint. ● Keynes published his Treatise on Probability in 1921, a notable contribution to the philosophical and mathematical underpinnings of , championing the important view that probabilities were no more or less than truth values intermediate between simple truth and falsity. 11 Epistemology ● Bayesian and frequentist interpret idea of probability differently. – Frequentist : an event's probability as the limit of its relative frequency in a large number of trials. ● i.e., parameters are constants. – Bayesian : 'probability' should be interpreted as degree of believability
    [Show full text]
  • Issue 93 ISSN 1027-488X
    NEWSLETTER OF THE EUROPEAN MATHEMATICAL SOCIETY Interview Yakov Sinai Features Mathematical Billiards and Chaos About ABC Societies The Catalan Photograph taken by Håkon Mosvold Larsen/NTB scanpix Mathematical Society September 2014 Issue 93 ISSN 1027-488X S E European M M Mathematical E S Society American Mathematical Society HILBERT’S FIFTH PROBLEM AND RELATED TOPICS Terence Tao, University of California In the fifth of his famous list of 23 problems, Hilbert asked if every topological group which was locally Euclidean was in fact a Lie group. Through the work of Gleason, Montgomery-Zippin, Yamabe, and others, this question was solved affirmatively. Subsequently, this structure theory was used to prove Gromov’s theorem on groups of polynomial growth, and more recently in the work of Hrushovski, Breuillard, Green, and the author on the structure of approximate groups. In this graduate text, all of this material is presented in a unified manner. Graduate Studies in Mathematics, Vol. 153 Aug 2014 338pp 9781470415648 Hardback €63.00 MATHEMATICAL METHODS IN QUANTUM MECHANICS With Applications to Schrödinger Operators, Second Edition Gerald Teschl, University of Vienna Quantum mechanics and the theory of operators on Hilbert space have been deeply linked since their beginnings in the early twentieth century. States of a quantum system correspond to certain elements of the configuration space and observables correspond to certain operators on the space. This book is a brief, but self-contained, introduction to the mathematical methods of quantum mechanics, with a view towards applications to Schrödinger operators. Graduate Studies in Mathematics, Vol. 157 Nov 2014 356pp 9781470417048 Hardback €61.00 MATHEMATICAL UNDERSTANDING OF NATURE Essays on Amazing Physical Phenomena and their Understanding by Mathematicians V.
    [Show full text]
  • Prefix-Free Kolmogorov Complexity
    Prefix-free complexity Properties of Kolmogorov complexity Information Theory and Statistics Lecture 7: Prefix-free Kolmogorov complexity Łukasz Dębowski [email protected] Ph. D. Programme 2013/2014 Project co-financed by the European Union within the framework of the European Social Fund Prefix-free complexity Properties of Kolmogorov complexity Towards algorithmic information theory In Shannon’s information theory, the amount of information carried by a random variable depends on the ascribed probability distribution. Andrey Kolmogorov (1903–1987), the founding father of modern probability theory, remarked that it is extremely hard to imagine a reasonable probability distribution for utterances in natural language but we can still estimate their information content. For that reason Kolmogorov proposed to define the information content of a particular string in a purely algorithmic way. A similar approach has been proposed a year earlier by Ray Solomonoff (1926–2009), who sought for optimal inductive inference. Project co-financed by the European Union within the framework of the European Social Fund Prefix-free complexity Properties of Kolmogorov complexity Kolmogorov complexity—analogue of Shannon entropy The fundamental object of algorithmic information theory is Kolmogorov complexity of a string, an analogue of Shannon entropy. It is defined as the length of the shortest program for a Turing machine such that the machine prints out the string and halts. The Turing machine itself is defined as a deterministic finite state automaton which moves along one or more infinite tapes filled with symbols from a fixed finite alphabet and which may read and write individual symbols. The concrete value of Kolmogorov complexity depends on the used Turing machine but many properties of Kolmogorov complexity are universal.
    [Show full text]
  • Jürgen K. Moser 1928–1999
    Jürgen K. Moser 1928–1999 A Biographical Memoir by Paul H. Rabinowitz ©2015 National Academy of Sciences. Any opinions expressed in this memoir are those of the author and do not necessarily reflect the views of the National Academy of Sciences. J Ü RGEN KURT MOSER July 4, 1928–December 17, 1999 Elected to the NAS, 1971 After the death of Jürgen Moser, one of the world’s great mathematicians, the American Mathematical Society published a memorial article about his research. It is well worth beginning here with a lightly edited version of the brief introductory remarks I wrote then: One of those rare people with a gift for seeing mathematics as a whole, Moser was very much aware of its connections to other branches of science. His research had a profound effect on mathematics as well as on astronomy and physics. He made deep and important contributions to an extremely broad range of questions in dynamical systems and celestial mechanics, partial differen- By Paul H. Rabinowitz tial equations, nonlinear functional analysis, differ- ential and complex geometry, and the calculus of variations. To those who knew him, Moser exemplified both a creative scientist and a human being. His standards were high and his taste impeccable. His papers were elegantly written. Not merely focused on his own path- breaking research, he worked successfully for the well-being of math- ematics in many ways. He stimulated several generations of younger people by his penetrating insights into their problems, scientific and otherwise, and his warm and wise counsel, concern, and encouragement. My own experience as his student was typical: then and afterwards I was made to feel like a member of his family.
    [Show full text]
  • A Brief Overview of Probability Theory in Data Science by Geert
    A brief overview of probability theory in data science Geert Verdoolaege 1Department of Applied Physics, Ghent University, Ghent, Belgium 2Laboratory for Plasma Physics, Royal Military Academy (LPP–ERM/KMS), Brussels, Belgium Tutorial 3rd IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis, 27-05-2019 Overview 1 Origins of probability 2 Frequentist methods and statistics 3 Principles of Bayesian probability theory 4 Monte Carlo computational methods 5 Applications Classification Regression analysis 6 Conclusions and references 2 Overview 1 Origins of probability 2 Frequentist methods and statistics 3 Principles of Bayesian probability theory 4 Monte Carlo computational methods 5 Applications Classification Regression analysis 6 Conclusions and references 3 Early history of probability Earliest traces in Western civilization: Jewish writings, Aristotle Notion of probability in law, based on evidence Usage in finance Usage and demonstration in gambling 4 Middle Ages World is knowable but uncertainty due to human ignorance William of Ockham: Ockham’s razor Probabilis: a supposedly ‘provable’ opinion Counting of authorities Later: degree of truth, a scale Quantification: Law, faith ! Bayesian notion Gaming ! frequentist notion 5 Quantification 17th century: Pascal, Fermat, Huygens Comparative testing of hypotheses Population statistics 1713: Ars Conjectandi by Jacob Bernoulli: Weak law of large numbers Principle of indifference De Moivre (1718): The Doctrine of Chances 6 Bayes and Laplace Paper by Thomas Bayes (1763): inversion
    [Show full text]