The Nucleolus, the Kernel, and the Bargaining Set: an Update∗
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Kernel and Image
Math 217 Worksheet 1 February: x3.1 Professor Karen E Smith (c)2015 UM Math Dept licensed under a Creative Commons By-NC-SA 4.0 International License. T Definitions: Given a linear transformation V ! W between vector spaces, we have 1. The source or domain of T is V ; 2. The target of T is W ; 3. The image of T is the subset of the target f~y 2 W j ~y = T (~x) for some x 2 Vg: 4. The kernel of T is the subset of the source f~v 2 V such that T (~v) = ~0g. Put differently, the kernel is the pre-image of ~0. Advice to the new mathematicians from an old one: In encountering new definitions and concepts, n m please keep in mind concrete examples you already know|in this case, think about V as R and W as R the first time through. How does the notion of a linear transformation become more concrete in this special case? Think about modeling your future understanding on this case, but be aware that there are other important examples and there are important differences (a linear map is not \a matrix" unless *source and target* are both \coordinate spaces" of column vectors). The goal is to become comfortable with the abstract idea of a vector space which embodies many n features of R but encompasses many other kinds of set-ups. A. For each linear transformation below, determine the source, target, image and kernel. 2 3 x1 3 (a) T : R ! R such that T (4x25) = x1 + x2 + x3. -
Math 120 Homework 3 Solutions
Math 120 Homework 3 Solutions Xiaoyu He, with edits by Prof. Church April 21, 2018 [Note from Prof. Church: solutions to starred problems may not include all details or all portions of the question.] 1.3.1* Let σ be the permutation 1 7! 3; 2 7! 4; 3 7! 5; 4 7! 2; 5 7! 1 and let τ be the permutation 1 7! 5; 2 7! 3; 3 7! 2; 4 7! 4; 5 7! 1. Find the cycle decompositions of each of the following permutations: σ; τ; σ2; στ; τσ; τ 2σ. The cycle decompositions are: σ = (135)(24) τ = (15)(23)(4) σ2 = (153)(2)(4) στ = (1)(2534) τσ = (1243)(5) τ 2σ = (135)(24): 1.3.7* Write out the cycle decomposition of each element of order 2 in S4. Elements of order 2 are also called involutions. There are six formed from a single transposition, (12); (13); (14); (23); (24); (34), and three from pairs of transpositions: (12)(34); (13)(24); (14)(23). 3.1.6* Define ' : R× ! {±1g by letting '(x) be x divided by the absolute value of x. Describe the fibers of ' and prove that ' is a homomorphism. The fibers of ' are '−1(1) = (0; 1) = fall positive realsg and '−1(−1) = (−∞; 0) = fall negative realsg. 3.1.7* Define π : R2 ! R by π((x; y)) = x + y. Prove that π is a surjective homomorphism and describe the kernel and fibers of π geometrically. The map π is surjective because e.g. π((x; 0)) = x. The kernel of π is the line y = −x through the origin. -
Discrete Topological Transformations for Image Processing Michel Couprie, Gilles Bertrand
Discrete Topological Transformations for Image Processing Michel Couprie, Gilles Bertrand To cite this version: Michel Couprie, Gilles Bertrand. Discrete Topological Transformations for Image Processing. Brimkov, Valentin E. and Barneva, Reneta P. Digital Geometry Algorithms, 2, Springer, pp.73-107, 2012, Lecture Notes in Computational Vision and Biomechanics, 978-94-007-4174-4. 10.1007/978-94- 007-4174-4_3. hal-00727377 HAL Id: hal-00727377 https://hal-upec-upem.archives-ouvertes.fr/hal-00727377 Submitted on 3 Sep 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Chapter 3 Discrete Topological Transformations for Image Processing Michel Couprie and Gilles Bertrand Abstract Topology-based image processing operators usually aim at trans- forming an image while preserving its topological characteristics. This chap- ter reviews some approaches which lead to efficient and exact algorithms for topological transformations in 2D, 3D and grayscale images. Some transfor- mations which modify topology in a controlled manner are also described. Finally, based on the framework of critical kernels, we show how to design a topologically sound parallel thinning algorithm guided by a priority function. 3.1 Introduction Topology-preserving operators, such as homotopic thinning and skeletoniza- tion, are used in many applications of image analysis to transform an object while leaving unchanged its topological characteristics. -
Viable Nash Equilibria: Formation and Defection (Feb 2020)
VIABLE NASH EQUILIBRIA: FORMATION AND DEFECTION (FEB 2020) EHUD KALAI In memory of John Nash Abstract. To be credible, economic analysis should restrict itself to the use of only those Nash equilibria that are viable. To assess the viability of an equilibrium , I study simple dual indices: a formation index, F (), that speci…es the number of loyalists needed to form ; and a defection index, D(), that speci…es the number of defectors that can sustain. Surprisingly, these simple indices (1) predict the performance of Nash equilibria in social systems and lab experiments, and (2) uncover new prop- erties of Nash equilibria and stability issues that have so far eluded game theory re…nements. JEL Classi…cation Codes: C0, C7, D5, D9. 1. Overview Current economic analysis often relies on the notion of a Nash equilibrium. Yet there are mixed opinions about the viability of this notion. On the one hand, many equilibria, referred to as viable in this paper, play critical roles in functioning social systems and perform well in lab and …eld experiments. Date: March 9, 2018, this version Feb 24, 2020. Key words and phrases. Normal form games, Nash equilibrium, Stability, Fault tolerance, Behavioral Economics. The author thanks the following people for helpful conversations: Nemanja Antic, Sunil Chopra, Vince Crawford, K…r Eliaz, Drew Fudenberg, Ronen Gradwohl, Yingni Guo, Adam Kalai, Fern Kalai, Martin Lariviere, Eric Maskin, Rosemarie Nagel, Andy Postlewaite, Larry Samuelson, David Schmeidler, James Schummer, Eran Shmaya, Joel Sobel, and Peyton Young; and seminar participants at the universities of the Basque Country, Oxford, Tel Aviv, Yale, Stanford, Berkeley, Stony Brook, Bar Ilan, the Technion, and the Hebrew University. -
Updating Beliefs When Evidence Is Open to Interpretation: Implications for Bias and Polarization∗
Updating Beliefs when Evidence is Open to Interpretation: Implications for Bias and Polarization∗ Roland G. Fryer, Jr., Philipp Harms, and Matthew O. Jacksony This Version: October 2017 Forthcoming in Journal of the European Economic Association Abstract We introduce a model in which agents observe signals about the state of the world, some of which are open to interpretation. Our decision makers first interpret each signal and then form a posterior on the sequence of interpreted signals. This `double updating' leads to confirmation bias and can lead agents who observe the same information to polarize. We explore the model's predictions in an on-line experiment in which individuals interpret research summaries about climate change and the death penalty. Consistent with the model, there is a significant relationship between an individual's prior and their interpretation of the summaries; and - even more striking - over half of the subjects exhibit polarizing behavior. JEL classification numbers: D10, D80, J15, J71, I30 Keywords: beliefs, polarization, learning, updating, Bayesian updating, biases, dis- crimination, decision making ∗We are grateful to Sandro Ambuehl, Ken Arrow, Tanaya Devi, Juan Dubra, Jon Eguia, Ben Golub, Richard Holden, Lawrence Katz, Peter Klibanoff, Scott Kominers, Franziska Michor, Giovanni Parmigiani, Matthew Rabin, Andrei Shleifer, and Joshua Schwartzein for helpful comments and suggestions. Adriano Fernandes provided exceptional research assistance. yFryer is at the Department of Economics, Harvard University, and the NBER, ([email protected]); Jackson is at the Department of Economics, Stanford University, the Santa Fe Institute, and CIFAR (jack- [email protected]); and Harms is at the Department of Mathematical Stochastics, University of Freiburg, ([email protected]). -
Kernel Methodsmethods Simple Idea of Data Fitting
KernelKernel MethodsMethods Simple Idea of Data Fitting Given ( xi,y i) i=1,…,n xi is of dimension d Find the best linear function w (hyperplane) that fits the data Two scenarios y: real, regression y: {-1,1}, classification Two cases n>d, regression, least square n<d, ridge regression New sample: x, < x,w> : best fit (regression), best decision (classification) 2 Primary and Dual There are two ways to formulate the problem: Primary Dual Both provide deep insight into the problem Primary is more traditional Dual leads to newer techniques in SVM and kernel methods 3 Regression 2 w = arg min ∑(yi − wo − ∑ xij w j ) W i j w = arg min (y − Xw )T (y − Xw ) W d(y − Xw )T (y − Xw ) = 0 dw ⇒ XT (y − Xw ) = 0 w = [w , w ,L, w ]T , ⇒ T T o 1 d X Xw = X y L T x = ,1[ x1, , xd ] , T −1 T ⇒ w = (X X) X y y = [y , y ,L, y ]T ) 1 2 n T −1 T xT y =< x (, X X) X y > 1 xT X = 2 M xT 4 n n×xd Graphical Interpretation ) y = Xw = Hy = X(XT X)−1 XT y = X(XT X)−1 XT y d X= n FICA Income X is a n (sample size) by d (dimension of data) matrix w combines the columns of X to best approximate y Combine features (FICA, income, etc.) to decisions (loan) H projects y onto the space spanned by columns of X Simplify the decisions to fit the features 5 Problem #1 n=d, exact solution n>d, least square, (most likely scenarios) When n < d, there are not enough constraints to determine coefficients w uniquely d X= n W 6 Problem #2 If different attributes are highly correlated (income and FICA) The columns become dependent Coefficients -
(Eds.) Foundations in Microeconomic Theory Hugo F
Matthew O. Jackson • Andrew McLennan (Eds.) Foundations in Microeconomic Theory Hugo F. Sonnenschein Matthew O. Jackson • Andrew McLennan (Eds.) Foundations in Microeconomic Theory A Volume in Honor of Hugo F. Sonnenschein ^ Springer Professor Matthew O. Jackson Professor Andrew McLennan Stanford University School of Economics Department of Economics University of Queensland Stanford, CA 94305-6072 Rm. 520, Colin Clark Building USA The University of Queensland [email protected] NSW 4072 Australia [email protected] ISBN: 978-3-540-74056-8 e-ISBN: 978-3"540-74057-5 Library of Congress Control Number: 2007941253 © 2008 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9,1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: WMX Design GmbH, Heidelberg Printed on acid-free paper 987654321 spnnger.com Contents Introduction 1 A Brief Biographical Sketch of Hugo F. Sonnenschein 5 1 Kevin C. Sontheimer on Hugo F. -
The Case of the Market for Non-Prime Mortgage-Based Securities Br
Made centro de pesquisa em macroeconomia das desigualdades Working Paper 18.01.2021 nº 003 Conventions and the process of market formation: the case of the market for non-prime mortgage-based securities Bruno Höfig Abstract 18.01.2021 nº 003 The article develops the hypothesis that conventions can be key to the development of markets for previously untraded financial assets. It argues that the absence of historical data on the performance of Bruno Höfig Ph.D. in Economics - SOAS, assets may induce agents to form beliefs in ways that University of London preclude the formation of markets for certain financial bhofi[email protected] instruments. Conversely, in the absence of trading, the data on the basis of which agents could revise their The author would like to thank Alfredo Saad-Filho, Pedro beliefs will not be produced, giving rise to a feedback Mendes Loureiro, Camila Alves loop that hinders the process of market-formation. de André, Leonardo Müller, The article contends that one of the ways of Tomás Gotta and Matías Vernengo for comments on overcoming this state is the development of earlier drafts of this article. The conventions about the distribution of future payofs. It usual disclaimers apply. then shows that the emergence of the conventions that credit rating agencies could reliably measure the riskiness of non-prime residential mortgage-securities (RMBSs), and that the FICO score could reliably give a quantitative expression of the creditworthiness of individual borrowers was a crucial enabling condition of the formation of a market for RMBSs in the United States. Keywords: conventional valuation, probability, mortgage-backed securities, credit rating agencies, credit scores. -
Abelian Categories
Abelian Categories Lemma. In an Ab-enriched category with zero object every finite product is coproduct and conversely. π1 Proof. Suppose A × B //A; B is a product. Define ι1 : A ! A × B and π2 ι2 : B ! A × B by π1ι1 = id; π2ι1 = 0; π1ι2 = 0; π2ι2 = id: It follows that ι1π1+ι2π2 = id (both sides are equal upon applying π1 and π2). To show that ι1; ι2 are a coproduct suppose given ' : A ! C; : B ! C. It φ : A × B ! C has the properties φι1 = ' and φι2 = then we must have φ = φid = φ(ι1π1 + ι2π2) = ϕπ1 + π2: Conversely, the formula ϕπ1 + π2 yields the desired map on A × B. An additive category is an Ab-enriched category with a zero object and finite products (or coproducts). In such a category, a kernel of a morphism f : A ! B is an equalizer k in the diagram k f ker(f) / A / B: 0 Dually, a cokernel of f is a coequalizer c in the diagram f c A / B / coker(f): 0 An Abelian category is an additive category such that 1. every map has a kernel and a cokernel, 2. every mono is a kernel, and every epi is a cokernel. In fact, it then follows immediatly that a mono is the kernel of its cokernel, while an epi is the cokernel of its kernel. 1 Proof of last statement. Suppose f : B ! C is epi and the cokernel of some g : A ! B. Write k : ker(f) ! B for the kernel of f. Since f ◦ g = 0 the map g¯ indicated in the diagram exists. -
23. Kernel, Rank, Range
23. Kernel, Rank, Range We now study linear transformations in more detail. First, we establish some important vocabulary. The range of a linear transformation f : V ! W is the set of vectors the linear transformation maps to. This set is also often called the image of f, written ran(f) = Im(f) = L(V ) = fL(v)jv 2 V g ⊂ W: The domain of a linear transformation is often called the pre-image of f. We can also talk about the pre-image of any subset of vectors U 2 W : L−1(U) = fv 2 V jL(v) 2 Ug ⊂ V: A linear transformation f is one-to-one if for any x 6= y 2 V , f(x) 6= f(y). In other words, different vector in V always map to different vectors in W . One-to-one transformations are also known as injective transformations. Notice that injectivity is a condition on the pre-image of f. A linear transformation f is onto if for every w 2 W , there exists an x 2 V such that f(x) = w. In other words, every vector in W is the image of some vector in V . An onto transformation is also known as an surjective transformation. Notice that surjectivity is a condition on the image of f. 1 Suppose L : V ! W is not injective. Then we can find v1 6= v2 such that Lv1 = Lv2. Then v1 − v2 6= 0, but L(v1 − v2) = 0: Definition Let L : V ! W be a linear transformation. The set of all vectors v such that Lv = 0W is called the kernel of L: ker L = fv 2 V jLv = 0g: 1 The notions of one-to-one and onto can be generalized to arbitrary functions on sets. -
Positive Semigroups of Kernel Operators
Positivity 12 (2008), 25–44 c 2007 Birkh¨auser Verlag Basel/Switzerland ! 1385-1292/010025-20, published online October 29, 2007 DOI 10.1007/s11117-007-2137-z Positivity Positive Semigroups of Kernel Operators Wolfgang Arendt Dedicated to the memory of H.H. Schaefer Abstract. Extending results of Davies and of Keicher on !p we show that the peripheral point spectrum of the generator of a positive bounded C0-semigroup of kernel operators on Lp is reduced to 0. It is shown that this implies con- vergence to an equilibrium if the semigroup is also irreducible and the fixed space non-trivial. The results are applied to elliptic operators. Mathematics Subject Classification (2000). 47D06, 47B33, 35K05. Keywords. Positive semigroups, Kernel operators, asymptotic behaviour, trivial peripheral spectrum. 0. Introduction Irreducibility is a fundamental notion in Perron-Frobenius Theory. It had been introduced in a direct way by Perron and Frobenius for matrices, but it was H. H. Schaefer who gave the definition via closed ideals. This turned out to be most fruit- ful and led to a wealth of deep and important results. For applications Ouhabaz’ very simple criterion for irreduciblity of semigroups defined by forms (see [Ouh05, Sec. 4.2] or [Are06]) is most useful. It shows that for practically all boundary con- ditions, a second order differential operator in divergence form generates a positive 2 N irreducible C0-semigroup on L (Ω) where Ωis an open, connected subset of R . The main question in Perron-Frobenius Theory, is to determine the asymp- totic behaviour of the semigroup. If the semigroup is bounded (in fact Abel bounded suffices), and if the fixed space is non-zero, then irreducibility is equiv- alent to convergence of the Ces`aro means to a strictly positive rank-one opera- tor, i.e. -
The Kernel of a Linear Transformation Is a Vector Subspace
The kernel of a linear transformation is a vector subspace. Given two vector spaces V and W and a linear transformation L : V ! W we define a set: Ker(L) = f~v 2 V j L(~v) = ~0g = L−1(f~0g) which we call the kernel of L. (some people call this the nullspace of L). Theorem As defined above, the set Ker(L) is a subspace of V , in particular it is a vector space. Proof Sketch We check the three conditions 1 Because we know L(~0) = ~0 we know ~0 2 Ker(L). 2 Let ~v1; ~v2 2 Ker(L) then we know L(~v1 + ~v2) = L(~v1) + L(~v2) = ~0 + ~0 = ~0 and so ~v1 + ~v2 2 Ker(L). 3 Let ~v 2 Ker(L) and a 2 R then L(a~v) = aL(~v) = a~0 = ~0 and so a~v 2 Ker(L). Math 3410 (University of Lethbridge) Spring 2018 1 / 7 Example - Kernels Matricies Describe and find a basis for the kernel, of the linear transformation, L, associated to 01 2 31 A = @3 2 1A 1 1 1 The kernel is precisely the set of vectors (x; y; z) such that L((x; y; z)) = (0; 0; 0), so 01 2 31 0x1 001 @3 2 1A @yA = @0A 1 1 1 z 0 but this is precisely the solutions to the system of equations given by A! So we find a basis by solving the system! Theorem If A is any matrix, then Ker(A), or equivalently Ker(L), where L is the associated linear transformation, is precisely the solutions ~x to the system A~x = ~0 This is immediate from the definition given our understanding of how to associate a system of equations to M~x = ~0: Math 3410 (University of Lethbridge) Spring 2018 2 / 7 The Kernel and Injectivity Recall that a function L : V ! W is injective if 8~v1; ~v2 2 V ; ((L(~v1) = L(~v2)) ) (~v1 = ~v2)) Theorem A linear transformation L : V ! W is injective if and only if Ker(L) = f~0g.