A Computational Logic This Is a Volume in the ACM MONOGRAPH SERIES
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AMATH 731: Applied Functional Analysis Lecture Notes
AMATH 731: Applied Functional Analysis Lecture Notes Sumeet Khatri November 24, 2014 Table of Contents List of Tables ................................................... v List of Theorems ................................................ ix List of Definitions ................................................ xii Preface ....................................................... xiii 1 Review of Real Analysis .......................................... 1 1.1 Convergence and Cauchy Sequences...............................1 1.2 Convergence of Sequences and Cauchy Sequences.......................1 2 Measure Theory ............................................... 2 2.1 The Concept of Measurability...................................3 2.1.1 Simple Functions...................................... 10 2.2 Elementary Properties of Measures................................ 11 2.2.1 Arithmetic in [0, ] .................................... 12 1 2.3 Integration of Positive Functions.................................. 13 2.4 Integration of Complex Functions................................. 14 2.5 Sets of Measure Zero......................................... 14 2.6 Positive Borel Measures....................................... 14 2.6.1 Vector Spaces and Topological Preliminaries...................... 14 2.6.2 The Riesz Representation Theorem........................... 14 2.6.3 Regularity Properties of Borel Measures........................ 14 2.6.4 Lesbesgue Measure..................................... 14 2.6.5 Continuity Properties of Measurable Functions................... -
List of Theorems from Geometry 2321, 2322
List of Theorems from Geometry 2321, 2322 Stephanie Hyland [email protected] April 24, 2010 There’s already a list of geometry theorems out there, but the course has changed since, so here’s a new one. They’re in order of ‘appearance in my notes’, which corresponds reasonably well to chronolog- ical order. 24 onwards is Hilary term stuff. The following theorems have actually been asked (in either summer or schol papers): 5, 7, 8, 17, 18, 20, 21, 22, 26, 27, 30 a), 31 (associative only), 35, 36 a), 37, 38, 39, 43, 45, 47, 48. Definitions are also asked, which aren’t included here. 1. On a finite-dimensional real vector space, the statement ‘V is open in M’ is independent of the choice of norm on M. ′ f f i 2. Let Rn ⊃ V −→ Rm be differentiable with f = (f 1, ..., f m). Then Rn −→ Rm, and f ′ = ∂f ∂xj f 3. Let M ⊃ V −→ N,a ∈ V . Then f is differentiable at a ⇒ f is continuous at a. 4. f = (f 1, ...f n) continuous ⇔ f i continuous, and same for differentiable. 5. The chain rule for functions on finite-dimensional real vector spaces. 6. The chain rule for functions of several real variables. f 7. Let Rn ⊃ V −→ R, V open. Then f is C1 ⇔ ∂f exists and is continuous, for i =1, ..., n. ∂xi 2 2 Rn f R 2 ∂ f ∂ f 8. Let ⊃ V −→ , V open. f is C . Then ∂xi∂xj = ∂xj ∂xi 9. (φ ◦ ψ)∗ = φ∗ ◦ ψ∗, where φ, ψ are maps of manifolds, and φ∗ is the push-forward of φ. -
Calculus I – Math
Math 380: Low-Dimensional Topology Instructor: Aaron Heap Office: South 330C E-mail: [email protected] Web Page: http://www.geneseo.edu/math/heap Textbook: Topology Now!, by Robert Messer & Philip Straffin. Course Info: We will cover topological equivalence, deformations, knots and links, surfaces, three- dimensional manifolds, and the fundamental group. Topics are subject to change depending on the progress of the class, and various topics may be skipped due to time constraints. An accurate reading schedule will be posted on the website, and you should check it often. By the time you take this course, most of you should be fairly comfortable with mathematical proofs. Although this course only has multivariable calculus, elementary linear algebra, and mathematical proofs as prerequisites, students are strongly encouraged to take abstract algebra (Math 330) prior to this course or concurrently. It requires a certain level of mathematical sophistication. There will be a lot of new terminology you must learn, and we will be doing a significant number of proofs. Please note that we will work on developing your independent reading skills in Mathematics and your ability to learn and use definitions and theorems. I certainly won't be able to cover in class all the material you will be required to learn. As a result, you will be expected to do a lot of reading. The reading assignments will be on topics to be discussed in the following lecture to enable you to ask focused questions in the class and to better understand the material. It is imperative that you keep up with the reading assignments. -
Infinite Computation, Co-Induction and Computational Logic
Infinite Computation, Co-induction and Computational Logic Gopal Gupta Neda Saeedloei, Brian DeVries, Richard Min, Kyle Marple, Feliks Klu´zniak Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080. Abstract. We give an overview of the coinductive logic programming paradigm. We discuss its applications to modeling ω-automata, model checking, verification, non-monotonic reasoning, developing SAT solvers, etc. We also discuss future research directions. 1 Introduction Coinduction is a technique for reasoning about unfounded sets [12], behavioral properties of programs [2], and proving liveness properties in model checking [16]. Coinduction also provides the foundation for lazy evaluation [9] and type inference [21] in functional programming as well as for interactive computing [33]. Coinduction is the dual of induction. Induction corresponds to well-founded structures that start from a basis which serves as the foundation: e.g., natural numbers are inductively defined via the base element zero and the successor function. Inductive definitions have 3 components: initiality, iteration and mini- mality. For example, the inductive definition of lists of numbers is as follows: (i) [] (empty list) is a list (initiality); (ii) [H|T] is a a list if T is a list and H is some number (iteration); and, (iii) the set of lists is the smallest set satisfying (i) and (ii) (minimality). Minimality implies that infinite-length lists of numbers are not members of the inductively defined set of lists of numbers. Inductive definitions correspond to least fixed point (LFP) interpretations of recursive definitions. Coinduction eliminates the initiality condition and replaces the minimality condition with maximality. Thus, the coinductive definition of a list of numbers is: (i) [H|T] is as a list if T is a list and H is some number (iteration); and, (ii) the set of lists is the largest set of lists satisfying (i) (maximality). -
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]. -
Math 025-1,2 List of Theorems and Definitions Fall 2010
Math 025-1,2 List of Theorems and Definitions Fall 2010 Here are the 19 theorems of Math 25: Domination Law (aka Monotonicity Law) If f and g are integrable functions on [a; b] such that R b R b f(x) ≤ g(x) for all x 2 [a; b], then a f(x) dx ≤ a g(x) dx. R b Positivity Law If f is a nonnegative, integrable function on [a; b], then a f(x) dx ≥ 0. Also, if f(x) is R b a nonnegative, continuous function on [a; b] and a f(x) dx = 0, then f(x) = 0 for all x 2 [a; b]. Max-Min Inequality If f(x) is an integrable function on [a; b], then Z b (min value of f(x) on [a; b]) · (b − a) ≤ f(x) dx ≤ (max value of f(x) on [a; b]) · (b − a): a Triangle Inequality (for Integrals) Let f :[a; b] ! R be integrable. Then Z b Z b f(x) dx ≤ jf(x)j dx: a a Mean Value Theorem for Integrals Let f : R ! R be continuous on [a; b]. Then there exists a number c 2 (a; b) such that 1 Z b f(c) = f(x) dx: b − a a Fundamental Theorem of Calculus (one part) Let f :[a; b] ! R be continuous and define F : R x [a; b] ! R by F (x) = a f(t) dt. Then F is differentiable (hence continuous) on [a; b] and F 0(x) = f(x). Fundamental Theorem of Calculus (other part) Let f :[a; b] ! R be continuous and let F be any antiderivative of f on [a; b]. -
Resources for Further Study
Resources for Further Study A number of valuable resources are available for further study of philosophical logic. In addition to the books and articles cited in the references at the end of each chapter included in this volume, there are four general categories of resources that can be consulted for information about the history and current research developments in philosophical logic. Additional materials can be found by soliciting advice from logicians, philosophers, and mathematicians at local colleges and universities. Logic Handbooks Many university and independent presses publish books on or related to mathematical and philo- sophical logic. There are also several special series of original monographs in logic that are worth investigating. The literature is too vast to justify a selection of the individual books that have con- tributed to the development of logic. We can nevertheless identify special categories of texts of special interest, beginning with handbooks and book series dedicated to logic and philosophical logic. Here are some recent relevant publications: Boyer, Robert S. (1988) A Computational Logic Handbook. Boston, MA: Academic Press. Sherwood, John C. (1960) Discourse of Reason: A Brief Handbook of Semantics and Logic. New York: Harper & Row. Handbook of Fuzzy Computation, ed. Enrique H. Ruspini, Piero P. Bonissone and Witold Pedrycz. Philadelphia, PA: Institute of Physics Publications, 1998. Handbook of Logic and Language, ed. Johan van Benthem and Alice ter Meulen. Cambridge, MA: MIT Press, 1997. Handbook of Logic in Artificial Intelligence and Logic Programming, ed. Dov Gabbay, C. J. Hogger, and J. A. Robinson. Oxford: Clarendon Press, 1993–8. Emmet, E. R. (1984) Handbook of Logic. -
Calculus I Teacher(S): Mr
Remote Learning Packet NB: Please keep all work produced this week. Details regarding how to turn in this work will be forthcoming. April 20 - 24, 2020 Course: 11 Calculus I Teacher(s): Mr. Simmons Weekly Plan: Monday, April 20 ⬜ Revise your proof of Fermat’s Theorem. Tuesday, April 21 ⬜ Extreme Value Theorem proof and diagram. Wednesday, April 22 ⬜ Diagrams for Fermat’s Theorem, Rolle’s Theorem, and the MVT Thursday, April 23 ⬜ Prove Rolle’s Theorem. Friday, April 24 ⬜ Prove the MVT. Statement of Academic Honesty I affirm that the work completed from the packet I affirm that, to the best of my knowledge, my is mine and that I completed it independently. child completed this work independently _______________________________________ _______________________________________ Student Signature Parent Signature Monday, April 20 I would like to apologize, because in the list of theorems that I sent you, there was a typo. In the hypotheses for two of the theorems, there were written nonstrict inequalities, but they should have been strict inequalities. I have corrected this in the new version. If you have felt particularly challenged by these proofs, that’s okay! I hope that this is an opportunity for you not to memorize a method and execute it perfectly, but rather to be challenged and to struggle with real mathematical problems. I highly encourage you to come to office hours (virtually) to ask questions about these problems. And feel free to email me as well! This week’s handout is a rewriting of those same theorems, along with one more, the Extreme Value Theorem. I apologize for all the changes, but I - along with the rest of you - am still adjusting to this new setup. -
Formal Proof—Getting Started Freek Wiedijk
Formal Proof—Getting Started Freek Wiedijk A List of 100 Theorems On the webpage [1] only eight entries are listed for Today highly nontrivial mathematics is routinely the first theorem, but in [2p] seventeen formaliza- being encoded in the computer, ensuring a reliabil- tions of the irrationality of 2 have been collected, ity that is orders of a magnitude larger than if one each with a short description of the proof assistant. had just used human minds. Such an encoding is When we analyze this list of theorems to see called a formalization, and a program that checks what systems occur most, it turns out that there are such a formalization for correctness is called a five proof assistants that have been significantly proof assistant. used for formalization of mathematics. These are: Suppose you have proved a theorem and you want to make certain that there are no mistakes proof assistant number of theorems formalized in the proof. Maybe already a couple of times a mistake has been found and you want to make HOL Light 69 sure that that will not happen again. Maybe you Mizar 45 fear that your intuition is misleading you and want ProofPower 42 to make sure that this is not the case. Or maybe Isabelle 40 you just want to bring your proof into the most Coq 39 pure and complete form possible. We will explain all together 80 in this article how to go about this. Although formalization has become a routine Currently in all systems together 80 theorems from activity, it still is labor intensive. -
A Gentle Tutorial for Programming on the ML-Level of Isabelle (Draft)
The Isabelle Cookbook A Gentle Tutorial for Programming on the ML-Level of Isabelle (draft) by Christian Urban with contributions from: Stefan Berghofer Jasmin Blanchette Sascha Bohme¨ Lukas Bulwahn Jeremy Dawson Rafal Kolanski Alexander Krauss Tobias Nipkow Andreas Schropp Christian Sternagel July 31, 2013 2 Contents Contentsi 1 Introduction1 1.1 Intended Audience and Prior Knowledge.................1 1.2 Existing Documentation..........................2 1.3 Typographic Conventions.........................2 1.4 How To Understand Isabelle Code.....................3 1.5 Aaaaargh! My Code Does not Work Anymore..............4 1.6 Serious Isabelle ML-Programming.....................4 1.7 Some Naming Conventions in the Isabelle Sources...........5 1.8 Acknowledgements.............................6 2 First Steps9 2.1 Including ML-Code.............................9 2.2 Printing and Debugging.......................... 10 2.3 Combinators................................ 14 2.4 ML-Antiquotations............................. 21 2.5 Storing Data in Isabelle.......................... 24 2.6 Summary.................................. 32 3 Isabelle Essentials 33 3.1 Terms and Types.............................. 33 3.2 Constructing Terms and Types Manually................. 38 3.3 Unification and Matching......................... 46 3.4 Sorts (TBD)................................. 55 3.5 Type-Checking............................... 55 3.6 Certified Terms and Certified Types.................... 57 3.7 Theorems.................................. 58 3.8 Theorem -
This Is a Paper in Whiuch Several Famous Theorems Are Poven by Using Ths Same Method
This is a paper in whiuch several famous theorems are poven by using ths same method. There formula due to Marston Morse which I think is exceptional. Vector Fields and Famous Theorems Daniel H. Gottlieb 1. Introduction. What do I mean ? Look at the example of Newton’s Law of Gravitation. Here is a mathematical statement of great simplicity which implies logically vast number of phe- nomena of incredible variety. For example, Galileo’s observation that objects of different weights fall to the Earth with the same acceleration, or Kepler’s laws governing the motion of the planets, or the daily movements of the tides are all due to the underlying notion of gravity. And this is established by deriving the the mathematical statements of these three classes of phenomena from Newton’s Law of Gravity. The mathematical relationship of falling objects, planets, and tides is defined by the beautiful fact that they follow from Newton’s Law by the same kind of argument. The derivation for falling objects is much simpler than the derivation of the tides, but the general method of proof is the same. This process of simple laws implying vast numbers of physical theorems repeats itself over and over in Physics, with Maxwell’s Equations and Conservation of Energy and Momentum. To a mathematician, this web of relationships stemming from a few sources is beautiful. But is it unreasonable ? This is the question I ask. Are there mathematical Laws ? That is are there theorems which imply large numbers of known, important, and beautiful results by using the same kind of proofs ? At thirteen I already knew what Mathematics was. -
Types in Logic, Mathematics, and Programming from the Handbook of Proof Theory
CHAPTER X Types in Logic, Mathematics and Programming Robert L. Constable Computer Science Department, Cornell University Ithaca, New York 1~853, USA Contents 1. Introduction ..................................... 684 2. Typed logic ..................................... 692 3. Type theory ..................................... 726 4. Typed programming languages ........................... 754 5. Conclusion ...................................... 766 6. Appendix ...................................... 768 References ........................................ 773 HANDBOOK OF PROOF THEORY Edited by S. R. Buss 1998 Elsevier Science B.V. All rights reserved 684 R. Constable 1. Introduction Proof theory and computer science are jointly engaged in a remarkable enter- prise. Together they provide the practical means to formalize vast amounts of mathematical knowledge. They have created the subject of automated reasoning and a digital computer based proof technology; these enable a diverse community of mathematicians, computer scientists, and educators to build a new artifact a globally distributed digital library of formalized mathematics. I think that this artifact signals the emergence of a new branch of mathematics, perhaps to be called Formal Mathematics. The theorems of this mathematics are completely formal and are processed digitally. They can be displayed as beautifully and legibly as journal quality mathematical text. At the heart of this library are completely formal proofs created with computer assistance. Their correctness is based on the axioms and rules of various foundational theories; this formal accounting of correctness supports the highest known standards of rigor and truth. The need to formally relate results in different foundational theories opens a new topic in proof theory and foundations of mathematics. Formal proofs of interesting theorems in current foundational theories are very large rigid objects. Creating them requires the speed and memory capacities of modern computer hardware and the expressiveness of modern software.