Boolean Algebras with Operators, Modal Logic, Random Graphs, Canonical Extension, Elementary Class, Variety
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Sketch Notes — Rings and Fields
Sketch Notes — Rings and Fields Neil Donaldson Fall 2018 Text • An Introduction to Abstract Algebra, John Fraleigh, 7th Ed 2003, Adison–Wesley (optional). Brief reminder of groups You should be familiar with the majority of what follows though there is a lot of time to remind yourself of the harder material! Try to complete the proofs of any results yourself. Definition. A binary structure (G, ·) is a set G together with a function · : G × G ! G. We say that G is closed under · and typically write · as juxtaposition.1 A semigroup is an associative binary structure: 8x, y, z 2 G, x(yz) = (xy)z A monoid is a semigroup with an identity element: 9e 2 G such that 8x 2 G, ex = xe = x A group is a monoid in which every element has an inverse: 8x 2 G, 9x−1 2 G such that xx−1 = x−1x = e A binary structure is commutative if 8x, y 2 G, xy = yx. A group with a commutative structure is termed abelian. A subgroup2 is a non-empty subset H ⊆ G which remains a group under the same binary operation. We write H ≤ G. Lemma. H is a subgroup of G if and only if it is a non-empty subset of G closed under multiplication and inverses in G. Standard examples of groups: sets of numbers under addition (Z, Q, R, nZ, etc.), matrix groups. Standard families: cyclic, symmetric, alternating, dihedral. 1You should be comfortable with both multiplicative and additive notation. 2More generally any substructure. 1 Cosets and Factor Groups Definition. -
1. Directed Graphs Or Quivers What Is Category Theory? • Graph Theory on Steroids • Comic Book Mathematics • Abstract Nonsense • the Secret Dictionary
1. Directed graphs or quivers What is category theory? • Graph theory on steroids • Comic book mathematics • Abstract nonsense • The secret dictionary Sets and classes: For S = fX j X2 = Xg, have S 2 S , S2 = S Directed graph or quiver: C = (C0;C1;@0 : C1 ! C0;@1 : C1 ! C0) Class C0 of objects, vertices, points, . Class C1 of morphisms, (directed) edges, arrows, . For x; y 2 C0, write C(x; y) := ff 2 C1 j @0f = x; @1f = yg f 2C1 tail, domain / @0f / @1f o head, codomain op Opposite or dual graph of C = (C0;C1;@0;@1) is C = (C0;C1;@1;@0) Graph homomorphism F : D ! C has object part F0 : D0 ! C0 and morphism part F1 : D1 ! C1 with @i ◦ F1(f) = F0 ◦ @i(f) for i = 0; 1. Graph isomorphism has bijective object and morphism parts. Poset (X; ≤): set X with reflexive, antisymmetric, transitive order ≤ Hasse diagram of poset (X; ≤): x ! y if y covers x, i.e., x 6= y and [x; y] = fx; yg, so x ≤ z ≤ y ) z = x or z = y. Hasse diagram of (N; ≤) is 0 / 1 / 2 / 3 / ::: Hasse diagram of (f1; 2; 3; 6g; j ) is 3 / 6 O O 1 / 2 1 2 2. Categories Category: Quiver C = (C0;C1;@0 : C1 ! C0;@1 : C1 ! C0) with: • composition: 8 x; y; z 2 C0 ; C(x; y) × C(y; z) ! C(x; z); (f; g) 7! g ◦ f • satisfying associativity: 8 x; y; z; t 2 C0 ; 8 (f; g; h) 2 C(x; y) × C(y; z) × C(z; t) ; h ◦ (g ◦ f) = (h ◦ g) ◦ f y iS qq <SSSS g qq << SSS f qqq h◦g < SSSS qq << SSS qq g◦f < SSS xqq << SS z Vo VV < x VVVV << VVVV < VVVV << h VVVV < h◦(g◦f)=(h◦g)◦f VVVV < VVV+ t • identities: 8 x; y; z 2 C0 ; 9 1y 2 C(y; y) : 8 f 2 C(x; y) ; 1y ◦ f = f and 8 g 2 C(y; z) ; g ◦ 1y = g f y o x MM MM 1y g MM MMM f MMM M& zo g y Example: N0 = fxg ; N1 = N ; 1x = 0 ; 8 m; n 2 N ; n◦m = m+n ; | one object, lots of arrows [monoid of natural numbers under addition] 4 x / x Equation: 3 + 5 = 4 + 4 Commuting diagram: 3 4 x / x 5 ( 1 if m ≤ n; Example: N1 = N ; 8 m; n 2 N ; jN(m; n)j = 0 otherwise | lots of objects, lots of arrows [poset (N; ≤) as a category] These two examples are small categories: have a set of morphisms. -
Graph Theory
1 Graph Theory “Begin at the beginning,” the King said, gravely, “and go on till you come to the end; then stop.” — Lewis Carroll, Alice in Wonderland The Pregolya River passes through a city once known as K¨onigsberg. In the 1700s seven bridges were situated across this river in a manner similar to what you see in Figure 1.1. The city’s residents enjoyed strolling on these bridges, but, as hard as they tried, no residentof the city was ever able to walk a route that crossed each of these bridges exactly once. The Swiss mathematician Leonhard Euler learned of this frustrating phenomenon, and in 1736 he wrote an article [98] about it. His work on the “K¨onigsberg Bridge Problem” is considered by many to be the beginning of the field of graph theory. FIGURE 1.1. The bridges in K¨onigsberg. J.M. Harris et al., Combinatorics and Graph Theory , DOI: 10.1007/978-0-387-79711-3 1, °c Springer Science+Business Media, LLC 2008 2 1. Graph Theory At first, the usefulness of Euler’s ideas and of “graph theory” itself was found only in solving puzzles and in analyzing games and other recreations. In the mid 1800s, however, people began to realize that graphs could be used to model many things that were of interest in society. For instance, the “Four Color Map Conjec- ture,” introduced by DeMorgan in 1852, was a famous problem that was seem- ingly unrelated to graph theory. The conjecture stated that four is the maximum number of colors required to color any map where bordering regions are colored differently. -
An Application of Graph Theory in Markov Chains Reliability Analysis
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by DSpace at VSB Technical University of Ostrava STATISTICS VOLUME: 12 j NUMBER: 2 j 2014 j JUNE An Application of Graph Theory in Markov Chains Reliability Analysis Pavel SKALNY Department of Applied Mathematics, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, 17. listopadu 15/2172, 708 33 Ostrava-Poruba, Czech Republic [email protected] Abstract. The paper presents reliability analysis which the production will be equal or greater than the indus- was realized for an industrial company. The aim of the trial partner demand. To deal with the task a Monte paper is to present the usage of discrete time Markov Carlo simulation of Discrete time Markov chains was chains and the flow in network approach. Discrete used. Markov chains a well-known method of stochastic mod- The goal of this paper is to present the Discrete time elling describes the issue. The method is suitable for Markov chain analysis realized on the system, which is many systems occurring in practice where we can easily not distributed in parallel. Since the analysed process distinguish various amount of states. Markov chains is more complicated the graph theory approach seems are used to describe transitions between the states of to be an appropriate tool. the process. The industrial process is described as a graph network. The maximal flow in the network cor- Graph theory has previously been applied to relia- responds to the production. The Ford-Fulkerson algo- bility, but for different purposes than we intend. -
Problems from Ring Theory
Home Page Problems From Ring Theory In the problems below, Z, Q, R, and C denote respectively the rings of integers, JJ II rational numbers, real numbers, and complex numbers. R generally denotes a ring, and I and J usually denote ideals. R[x],R[x, y],... denote rings of polynomials. rad R is defined to be {r ∈ R : rn = 0 for some positive integer n} , where R is a ring; rad R is called the nil radical or just the radical of R . J I Problem 0. Let b be a nilpotent element of the ring R . Prove that 1 + b is an invertible element Page 1 of 14 of R . Problem 1. Let R be a ring with more than one element such that aR = R for every nonzero Go Back element a ∈ R. Prove that R is a division ring. Problem 2. If (m, n) = 1 , show that the ring Z/(mn) contains at least two idempotents other than Full Screen the zero and the unit. Problem 3. If a and b are elements of a commutative ring with identity such that a is invertible Print and b is nilpotent, then a + b is invertible. Problem 4. Let R be a ring which has no nonzero nilpotent elements. Prove that every idempotent Close element of R commutes with every element of R. Quit Home Page Problem 5. Let A be a division ring, B be a proper subring of A such that a−1Ba ⊆ B for all a 6= 0 . Prove that B is contained in the center of A . -
On Semilattice Structure of Mizar Types
FORMALIZED MATHEMATICS Volume 11, Number 4, 2003 University of Białystok On Semilattice Structure of Mizar Types Grzegorz Bancerek Białystok Technical University Summary. The aim of this paper is to develop a formal theory of Mizar types. The presented theory is an approach to the structure of Mizar types as a sup-semilattice with widening (subtyping) relation as the order. It is an abstrac- tion from the existing implementation of the Mizar verifier and formalization of the ideas from [9]. MML Identifier: ABCMIZ 0. The articles [20], [14], [24], [26], [23], [25], [3], [21], [1], [11], [12], [16], [10], [13], [18], [15], [4], [2], [19], [22], [5], [6], [7], [8], and [17] provide the terminology and notation for this paper. 1. Semilattice of Widening Let us mention that every non empty relational structure which is trivial and reflexive is also complete. Let T be a relational structure. A type of T is an element of T . Let T be a relational structure. We say that T is Noetherian if and only if: (Def. 1) The internal relation of T is reversely well founded. Let us observe that every non empty relational structure which is trivial is also Noetherian. Let T be a non empty relational structure. Let us observe that T is Noethe- rian if and only if the condition (Def. 2) is satisfied. (Def. 2) Let A be a non empty subset of T . Then there exists an element a of T such that a ∈ A and for every element b of T such that b ∈ A holds a 6< b. -
Math 250A: Groups, Rings, and Fields. H. W. Lenstra Jr. 1. Prerequisites
Math 250A: Groups, rings, and fields. H. W. Lenstra jr. 1. Prerequisites This section consists of an enumeration of terms from elementary set theory and algebra. You are supposed to be familiar with their definitions and basic properties. Set theory. Sets, subsets, the empty set , operations on sets (union, intersection, ; product), maps, composition of maps, injective maps, surjective maps, bijective maps, the identity map 1X of a set X, inverses of maps. Relations, equivalence relations, equivalence classes, partial and total orderings, the cardinality #X of a set X. The principle of math- ematical induction. Zorn's lemma will be assumed in a number of exercises. Later in the course the terminology and a few basic results from point set topology may come in useful. Group theory. Groups, multiplicative and additive notation, the unit element 1 (or the zero element 0), abelian groups, cyclic groups, the order of a group or of an element, Fermat's little theorem, products of groups, subgroups, generators for subgroups, left cosets aH, right cosets, the coset spaces G=H and H G, the index (G : H), the theorem of n Lagrange, group homomorphisms, isomorphisms, automorphisms, normal subgroups, the factor group G=N and the canonical map G G=N, homomorphism theorems, the Jordan- ! H¨older theorem (see Exercise 1.4), the commutator subgroup [G; G], the center Z(G) (see Exercise 1.12), the group Aut G of automorphisms of G, inner automorphisms. Examples of groups: the group Sym X of permutations of a set X, the symmetric group S = Sym 1; 2; : : : ; n , cycles of permutations, even and odd permutations, the alternating n f g group A , the dihedral group D = (1 2 : : : n); (1 n 1)(2 n 2) : : : , the Klein four group n n h − − i V , the quaternion group Q = 1; i; j; ij (with ii = jj = 1, ji = ij) of order 4 8 { g − − 8, additive groups of rings, the group Gl(n; R) of invertible n n-matrices over a ring R. -
Ring (Mathematics) 1 Ring (Mathematics)
Ring (mathematics) 1 Ring (mathematics) In mathematics, a ring is an algebraic structure consisting of a set together with two binary operations usually called addition and multiplication, where the set is an abelian group under addition (called the additive group of the ring) and a monoid under multiplication such that multiplication distributes over addition.a[›] In other words the ring axioms require that addition is commutative, addition and multiplication are associative, multiplication distributes over addition, each element in the set has an additive inverse, and there exists an additive identity. One of the most common examples of a ring is the set of integers endowed with its natural operations of addition and multiplication. Certain variations of the definition of a ring are sometimes employed, and these are outlined later in the article. Polynomials, represented here by curves, form a ring under addition The branch of mathematics that studies rings is known and multiplication. as ring theory. Ring theorists study properties common to both familiar mathematical structures such as integers and polynomials, and to the many less well-known mathematical structures that also satisfy the axioms of ring theory. The ubiquity of rings makes them a central organizing principle of contemporary mathematics.[1] Ring theory may be used to understand fundamental physical laws, such as those underlying special relativity and symmetry phenomena in molecular chemistry. The concept of a ring first arose from attempts to prove Fermat's last theorem, starting with Richard Dedekind in the 1880s. After contributions from other fields, mainly number theory, the ring notion was generalized and firmly established during the 1920s by Emmy Noether and Wolfgang Krull.[2] Modern ring theory—a very active mathematical discipline—studies rings in their own right. -
Optimisation
Optimisation Embryonic notes for the course A4B33OPT This text is incomplete and may be added to and improved during the semester. This version: 25th February 2015 Tom´aˇsWerner Translated from Czech to English by Libor Spaˇcekˇ Czech Technical University Faculty of Electrical Engineering Contents 1 Formalising Optimisation Tasks8 1.1 Mathematical notation . .8 1.1.1 Sets ......................................8 1.1.2 Mappings ...................................9 1.1.3 Functions and mappings of several real variables . .9 1.2 Minimum of a function over a set . 10 1.3 The general problem of continuous optimisation . 11 1.4 Exercises . 13 2 Matrix Algebra 14 2.1 Matrix operations . 14 2.2 Transposition and symmetry . 15 2.3 Rankandinversion .................................. 15 2.4 Determinants ..................................... 16 2.5 Matrix of a single column or a single row . 17 2.6 Matrixsins ...................................... 17 2.6.1 An expression is nonsensical because of the matrices dimensions . 17 2.6.2 The use of non-existent matrix identities . 18 2.6.3 Non equivalent manipulations of equations and inequalities . 18 2.6.4 Further ideas for working with matrices . 19 2.7 Exercises . 19 3 Linearity 22 3.1 Linear subspaces . 22 3.2 Linearmapping.................................... 22 3.2.1 The range and the null space . 23 3.3 Affine subspace and mapping . 24 3.4 Exercises . 25 4 Orthogonality 27 4.1 Scalarproduct..................................... 27 4.2 Orthogonal vectors . 27 4.3 Orthogonal subspaces . 28 4.4 The four fundamental subspaces of a matrix . 28 4.5 Matrix with orthonormal columns . 29 4.6 QR decomposition . 30 4.6.1 (?) Gramm-Schmidt orthonormalisation . -
Graph Algorithms in Bioinformatics an Introduction to Bioinformatics Algorithms Outline
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Graph Algorithms in Bioinformatics An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Outline 1. Introduction to Graph Theory 2. The Hamiltonian & Eulerian Cycle Problems 3. Basic Biological Applications of Graph Theory 4. DNA Sequencing 5. Shortest Superstring & Traveling Salesman Problems 6. Sequencing by Hybridization 7. Fragment Assembly & Repeats in DNA 8. Fragment Assembly Algorithms An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Section 1: Introduction to Graph Theory An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Knight Tours • Knight Tour Problem: Given an 8 x 8 chessboard, is it possible to find a path for a knight that visits every square exactly once and returns to its starting square? • Note: In chess, a knight may move only by jumping two spaces in one direction, followed by a jump one space in a perpendicular direction. http://www.chess-poster.com/english/laws_of_chess.htm An Introduction to Bioinformatics Algorithms www.bioalgorithms.info 9th Century: Knight Tours Discovered An Introduction to Bioinformatics Algorithms www.bioalgorithms.info 18th Century: N x N Knight Tour Problem • 1759: Berlin Academy of Sciences proposes a 4000 francs prize for the solution of the more general problem of finding a knight tour on an N x N chessboard. • 1766: The problem is solved by Leonhard Euler (pronounced “Oiler”). • The prize was never awarded since Euler was Director of Mathematics at Berlin Academy and was deemed ineligible. Leonhard Euler http://commons.wikimedia.org/wiki/File:Leonhard_Euler_by_Handmann.png An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Introduction to Graph Theory • A graph is a collection (V, E) of two sets: • V is simply a set of objects, which we call the vertices of G. -
Quasigroups, Right Quasigroups, Coverings and Representations Tengku Simonthy Renaldin Fuad Iowa State University
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1993 Quasigroups, right quasigroups, coverings and representations Tengku Simonthy Renaldin Fuad Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Mathematics Commons Recommended Citation Fuad, Tengku Simonthy Renaldin, "Quasigroups, right quasigroups, coverings and representations " (1993). Retrospective Theses and Dissertations. 10427. https://lib.dr.iastate.edu/rtd/10427 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. -
Graph Theory Approach to the Vulnerability of Transportation Networks
algorithms Article Graph Theory Approach to the Vulnerability of Transportation Networks Sambor Guze Department of Mathematics, Gdynia Maritime University, 81-225 Gdynia, Poland; [email protected]; Tel.: +48-608-034-109 Received: 24 November 2019; Accepted: 10 December 2019; Published: 12 December 2019 Abstract: Nowadays, transport is the basis for the functioning of national, continental, and global economies. Thus, many governments recognize it as a critical element in ensuring the daily existence of societies in their countries. Those responsible for the proper operation of the transport sector must have the right tools to model, analyze, and optimize its elements. One of the most critical problems is the need to prevent bottlenecks in transport networks. Thus, the main aim of the article was to define the parameters characterizing the transportation network vulnerability and select algorithms to support their search. The parameters proposed are based on characteristics related to domination in graph theory. The domination, edge-domination concepts, and related topics, such as bondage-connected and weighted bondage-connected numbers, were applied as the tools for searching and identifying the bottlenecks in transportation networks. Furthermore, the algorithms for finding the minimal dominating set and minimal (maximal) weighted dominating sets are proposed. This way, the exemplary academic transportation network was analyzed in two cases: stationary and dynamic. Some conclusions are presented. The main one is the fact that the methods given in this article are universal and applicable to both small and large-scale networks. Moreover, the approach can support the dynamic analysis of bottlenecks in transport networks. Keywords: vulnerability; domination; edge-domination; transportation networks; bondage-connected number; weighted bondage-connected number 1.