Functions of Bounded Variation on “Good” Metric Spaces
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Vector Measures with Variation in a Banach Function Space
VECTOR MEASURES WITH VARIATION IN A BANACH FUNCTION SPACE O. BLASCO Departamento de An´alisis Matem´atico, Universidad de Valencia, Doctor Moliner, 50 E-46100 Burjassot (Valencia), Spain E-mail:[email protected] PABLO GREGORI Departament de Matem`atiques, Universitat Jaume I de Castell´o, Campus Riu Sec E-12071 Castell´o de la Plana (Castell´o), Spain E-mail:[email protected] Let E be a Banach function space and X be an arbitrary Banach space. Denote by E(X) the K¨othe-Bochner function space defined as the set of measurable functions f :Ω→ X such that the nonnegative functions fX :Ω→ [0, ∞) are in the lattice E. The notion of E-variation of a measure —which allows to recover the p- variation (for E = Lp), Φ-variation (for E = LΦ) and the general notion introduced by Gresky and Uhl— is introduced. The space of measures of bounded E-variation VE (X) is then studied. It is shown, amongother thingsand with some restriction ∗ ∗ of absolute continuity of the norms, that (E(X)) = VE (X ), that VE (X) can be identified with space of cone absolutely summingoperators from E into X and that E(X)=VE (X) if and only if X has the RNP property. 1. Introduction The concept of variation in the frame of vector measures has been fruitful in several areas of the functional analysis, such as the description of the duality of vector-valued function spaces such as certain K¨othe-Bochner function spaces (Gretsky and Uhl10, Dinculeanu7), the reformulation of operator ideals such as the cone absolutely summing operators (Blasco4), and the Hardy spaces of harmonic function (Blasco2,3). -
The Matroid Theorem We First Review Our Definitions: a Subset System Is A
CMPSCI611: The Matroid Theorem Lecture 5 We first review our definitions: A subset system is a set E together with a set of subsets of E, called I, such that I is closed under inclusion. This means that if X ⊆ Y and Y ∈ I, then X ∈ I. The optimization problem for a subset system (E, I) has as input a positive weight for each element of E. Its output is a set X ∈ I such that X has at least as much total weight as any other set in I. A subset system is a matroid if it satisfies the exchange property: If i and i0 are sets in I and i has fewer elements than i0, then there exists an element e ∈ i0 \ i such that i ∪ {e} ∈ I. 1 The Generic Greedy Algorithm Given any finite subset system (E, I), we find a set in I as follows: • Set X to ∅. • Sort the elements of E by weight, heaviest first. • For each element of E in this order, add it to X iff the result is in I. • Return X. Today we prove: Theorem: For any subset system (E, I), the greedy al- gorithm solves the optimization problem for (E, I) if and only if (E, I) is a matroid. 2 Theorem: For any subset system (E, I), the greedy al- gorithm solves the optimization problem for (E, I) if and only if (E, I) is a matroid. Proof: We will show first that if (E, I) is a matroid, then the greedy algorithm is correct. Assume that (E, I) satisfies the exchange property. -
CONTINUITY in the ALEXIEWICZ NORM Dedicated to Prof. J
131 (2006) MATHEMATICA BOHEMICA No. 2, 189{196 CONTINUITY IN THE ALEXIEWICZ NORM Erik Talvila, Abbotsford (Received October 19, 2005) Dedicated to Prof. J. Kurzweil on the occasion of his 80th birthday Abstract. If f is a Henstock-Kurzweil integrable function on the real line, the Alexiewicz norm of f is kfk = sup j I fj where the supremum is taken over all intervals I ⊂ . Define I the translation τx by τxfR(y) = f(y − x). Then kτxf − fk tends to 0 as x tends to 0, i.e., f is continuous in the Alexiewicz norm. For particular functions, kτxf − fk can tend to 0 arbitrarily slowly. In general, kτxf − fk > osc fjxj as x ! 0, where osc f is the oscillation of f. It is shown that if F is a primitive of f then kτxF − F k kfkjxj. An example 1 6 1 shows that the function y 7! τxF (y) − F (y) need not be in L . However, if f 2 L then kτxF − F k1 6 kfk1jxj. For a positive weight function w on the real line, necessary and sufficient conditions on w are given so that k(τxf − f)wk ! 0 as x ! 0 whenever fw is Henstock-Kurzweil integrable. Applications are made to the Poisson integral on the disc and half-plane. All of the results also hold with the distributional Denjoy integral, which arises from the completion of the space of Henstock-Kurzweil integrable functions as a subspace of Schwartz distributions. Keywords: Henstock-Kurzweil integral, Alexiewicz norm, distributional Denjoy integral, Poisson integral MSC 2000 : 26A39, 46Bxx 1. -
Cardinality Constrained Combinatorial Optimization: Complexity and Polyhedra
Takustraße 7 Konrad-Zuse-Zentrum D-14195 Berlin-Dahlem fur¨ Informationstechnik Berlin Germany RUDIGER¨ STEPHAN1 Cardinality Constrained Combinatorial Optimization: Complexity and Polyhedra 1Email: [email protected] ZIB-Report 08-48 (December 2008) Cardinality Constrained Combinatorial Optimization: Complexity and Polyhedra R¨udigerStephan Abstract Given a combinatorial optimization problem and a subset N of natural numbers, we obtain a cardinality constrained version of this problem by permitting only those feasible solutions whose cardinalities are elements of N. In this paper we briefly touch on questions that addresses common grounds and differences of the complexity of a combinatorial optimization problem and its cardinality constrained version. Afterwards we focus on polytopes associated with cardinality constrained combinatorial optimiza- tion problems. Given an integer programming formulation for a combina- torial optimization problem, by essentially adding Gr¨otschel’s cardinality forcing inequalities [11], we obtain an integer programming formulation for its cardinality restricted version. Since the cardinality forcing inequal- ities in their original form are mostly not facet defining for the associated polyhedra, we discuss possibilities to strengthen them. In [13] a variation of the cardinality forcing inequalities were successfully integrated in the system of linear inequalities for the matroid polytope to provide a com- plete linear description of the cardinality constrained matroid polytope. We identify this polytope as a master polytope for our class of problems, since many combinatorial optimization problems can be formulated over the intersection of matroids. 1 Introduction, Basics, and Complexity Given a combinatorial optimization problem and a subset N of natural numbers, we obtain a cardinality constrained version of this problem by permitting only those feasible solutions whose cardinalities are elements of N. -
Some Properties of AP Weight Function
Journal of the Institute of Engineering, 2016, 12(1): 210-213 210 TUTA/IOE/PCU © TUTA/IOE/PCU Printed in Nepal Some Properties of AP Weight Function Santosh Ghimire Department of Engineering Science and Humanities, Institute of Engineering Pulchowk Campus, Tribhuvan University, Kathmandu, Nepal Corresponding author: [email protected] Received: June 20, 2016 Revised: July 25, 2016 Accepted: July 28, 2016 Abstract: In this paper, we briefly discuss the theory of weights and then define A1 and Ap weight functions. Finally we prove some of the properties of AP weight function. Key words: A1 weight function, Maximal functions, Ap weight function. 1. Introduction The theory of weights play an important role in various fields such as extrapolation theory, vector-valued inequalities and estimates for certain class of non linear differential equation. Moreover, they are very useful in the study of boundary value problems for Laplace's equation in Lipschitz domains. In 1970, Muckenhoupt characterized positive functions w for which the Hardy-Littlewood maximal operator M maps Lp(Rn, w(x)dx) to itself. Muckenhoupt's characterization actually gave the better understanding of theory of weighted inequalities which then led to the introduction of Ap class and consequently the development of weighted inequalities. 2. Definitions n Definition: A locally integrable function on R that takes values in the interval (0,∞) almost everywhere is called a weight. So by definition a weight function can be zero or infinity only on a set whose Lebesgue measure is zero. We use the notation to denote the w-measure of the set E and we reserve the notation Lp(Rn,w) or Lp(w) for the weighted L p spaces. -
Importance Sampling
Chapter 6 Importance sampling 6.1 The basics To movtivate our discussion consider the following situation. We want to use Monte Carlo to compute µ = E[X]. There is an event E such that P (E) is small but X is small outside of E. When we run the usual Monte Carlo algorithm the vast majority of our samples of X will be outside E. But outside of E, X is close to zero. Only rarely will we get a sample in E where X is not small. Most of the time we think of our problem as trying to compute the mean of some random variable X. For importance sampling we need a little more structure. We assume that the random variable we want to compute the mean of is of the form f(X~ ) where X~ is a random vector. We will assume that the joint distribution of X~ is absolutely continous and let p(~x) be the density. (Everything we will do also works for the case where the random vector X~ is discrete.) So we focus on computing Ef(X~ )= f(~x)p(~x)dx (6.1) Z Sometimes people restrict the region of integration to some subset D of Rd. (Owen does this.) We can (and will) instead just take p(x) = 0 outside of D and take the region of integration to be Rd. The idea of importance sampling is to rewrite the mean as follows. Let q(x) be another probability density on Rd such that q(x) = 0 implies f(x)p(x) = 0. -
Adaptively Weighted Maximum Likelihood Estimation of Discrete Distributions
Unicentre CH-1015 Lausanne http://serval.unil.ch Year : 2010 ADAPTIVELY WEIGHTED MAXIMUM LIKELIHOOD ESTIMATION OF DISCRETE DISTRIBUTIONS Michael AMIGUET Michael AMIGUET, 2010, ADAPTIVELY WEIGHTED MAXIMUM LIKELIHOOD ESTIMATION OF DISCRETE DISTRIBUTIONS Originally published at : Thesis, University of Lausanne Posted at the University of Lausanne Open Archive. http://serval.unil.ch Droits d’auteur L'Université de Lausanne attire expressément l'attention des utilisateurs sur le fait que tous les documents publiés dans l'Archive SERVAL sont protégés par le droit d'auteur, conformément à la loi fédérale sur le droit d'auteur et les droits voisins (LDA). A ce titre, il est indispensable d'obtenir le consentement préalable de l'auteur et/ou de l’éditeur avant toute utilisation d'une oeuvre ou d'une partie d'une oeuvre ne relevant pas d'une utilisation à des fins personnelles au sens de la LDA (art. 19, al. 1 lettre a). A défaut, tout contrevenant s'expose aux sanctions prévues par cette loi. Nous déclinons toute responsabilité en la matière. Copyright The University of Lausanne expressly draws the attention of users to the fact that all documents published in the SERVAL Archive are protected by copyright in accordance with federal law on copyright and similar rights (LDA). Accordingly it is indispensable to obtain prior consent from the author and/or publisher before any use of a work or part of a work for purposes other than personal use within the meaning of LDA (art. 19, para. 1 letter a). Failure to do so will expose offenders to the sanctions laid down by this law. -
3 May 2012 Inner Product Quadratures
Inner product quadratures Yu Chen Courant Institute of Mathematical Sciences New York University Aug 26, 2011 Abstract We introduce a n-term quadrature to integrate inner products of n functions, as opposed to a Gaussian quadrature to integrate 2n functions. We will characterize and provide computataional tools to construct the inner product quadrature, and establish its connection to the Gaussian quadrature. Contents 1 The inner product quadrature 2 1.1 Notation.................................... 2 1.2 A n-termquadratureforinnerproducts. 4 2 Construct the inner product quadrature 4 2.1 Polynomialcase................................ 4 2.2 Arbitraryfunctions .............................. 6 arXiv:1205.0601v1 [math.NA] 3 May 2012 3 Product law and minimal functions 7 3.1 Factorspaces ................................. 8 3.2 Minimalfunctions............................... 11 3.3 Fold data into Gramians - signal processing . 13 3.4 Regularization................................. 15 3.5 Type-3quadraturesforintegralequations. .... 15 4 Examples 16 4.1 Quadratures for non-positive definite weights . ... 16 4.2 Powerfunctions,HankelGramians . 16 4.3 Exponentials kx,hyperbolicGramians ................... 18 1 5 Generalizations and applications 19 5.1 Separation principle of imaging . 20 5.2 Quadratures in higher dimensions . 21 5.3 Deflationfor2-Dquadraturedesign . 22 1 The inner product quadrature We consider three types of n-term Gaussian quadratures in this paper, Type-1: to integrate 2n functions in interval [a, b]. • Type-2: to integrate n2 inner products of n functions. • Type-3: to integrate n functions against n weights. • For these quadratures, the weight functions u are not required positive definite. Type-1 is the classical Guassian quadrature, Type-2 is the inner product quadrature, and Type-3 finds applications in imaging and sensing, and discretization of integral equations. -
Chapter 2. Functions of Bounded Variation §1. Monotone Functions
Chapter 2. Functions of Bounded Variation §1. Monotone Functions Let I be an interval in IR. A function f : I → IR is said to be increasing (strictly increasing) if f(x) ≤ f(y)(f(x) < f(y)) whenever x, y ∈ I and x < y. A function f : I → IR is said to be decreasing (strictly decreasing) if f(x) ≥ f(y)(f(x) > f(y)) whenever x, y ∈ I and x < y. A function f : I → IR is said to be monotone if f is either increasing or decreasing. In this section we will show that a monotone function is differentiable almost every- where. For this purpose, we first establish Vitali covering lemma. Let E be a subset of IR, and let Γ be a collection of closed intervals in IR. We say that Γ covers E in the sense of Vitali if for each δ > 0 and each x ∈ E, there exists an interval I ∈ Γ such that x ∈ I and `(I) < δ. Theorem 1.1. Let E be a subset of IR with λ∗(E) < ∞ and Γ a collection of closed intervals that cover E in the sense of Vitali. Then, for given ε > 0, there exists a finite disjoint collection {I1,...,IN } of intervals in Γ such that ∗ N λ E \ ∪n=1In < ε. Proof. Let G be an open set containing E such that λ(G) < ∞. Since Γ is a Vitali covering of E, we may assume that each I ∈ Γ is contained in G. We choose a sequence (In)n=1,2,... of disjoint intervals from Γ recursively as follows. -
Section 3.5 of Folland’S Text, Which Covers Functions of Bounded Variation on the Real Line and Related Topics
REAL ANALYSIS LECTURE NOTES: 3.5 FUNCTIONS OF BOUNDED VARIATION CHRISTOPHER HEIL 3.5.1 Definition and Basic Properties of Functions of Bounded Variation We will expand on the first part of Section 3.5 of Folland's text, which covers functions of bounded variation on the real line and related topics. We begin with functions defined on finite closed intervals in R (note that Folland's ap- proach and notation is slightly different, as he begins with functions defined on R and uses TF (x) instead of our V [f; a; b]). Definition 1. Let f : [a; b] ! C be given. Given any finite partition Γ = fa = x0 < · · · < xn = bg of [a; b], set n SΓ = jf(xi) − f(xi−1)j: i=1 X The variation of f over [a; b] is V [f; a; b] = sup SΓ : Γ is a partition of [a; b] : The function f has bounded variation on [a; b] if V [f; a; b] < 1. We set BV[a; b] = f : [a; b] ! C : f has bounded variation on [a; b] : Note that in this definition we are considering f to be defined at all poin ts, and not just to be an equivalence class of functions that are equal a.e. The idea of the variation of f is that is represents the total vertical distance traveled by a particle that moves along the graph of f from (a; f(a)) to (b; f(b)). Exercise 2. (a) Show that if f : [a; b] ! C, then V [f; a; b] ≥ jf(b) − f(a)j. -
Measure Algebras and Functions of Bounded Variation on Idempotent Semigroupso
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 163, January 1972 MEASURE ALGEBRAS AND FUNCTIONS OF BOUNDED VARIATION ON IDEMPOTENT SEMIGROUPSO BY STEPHEN E. NEWMAN Abstract. Our main result establishes an isomorphism between all functions on an idempotent semigroup S with identity, under the usual addition and multiplication, and all finitely additive measures on a certain Boolean algebra of subsets of S, under the usual addition and a convolution type multiplication. Notions of a function of bounded variation on 5 and its variation norm are defined in such a way that the above isomorphism, restricted to the functions of bounded variation, is an isometry onto the set of all bounded measures. Our notion of a function of bounded variation is equivalent to the classical notion in case S is the unit interval and the "product" of two numbers in S is their maximum. 1. Introduction. This paper is motivated by the pair of closely related Banach algebras described below. The interval [0, 1] is an idempotent semigroup when endowed with the operation of maximum multiplication (.vj = max (x, y) for all x, y in [0, 1]). We let A denote the Boolean algebra (or set algebra) consisting of all finite unions of left-open, right-closed intervals (including the single point 0) contained in the interval [0, 1]. The space M(A) of all bounded, finitely additive measures on A is a Banach space with the usual total variation norm. The nature of the set algebra A and the idempotent multiplication given the interval [0, 1] make it possible to define a convolution multiplication in M(A) which makes it a Banach algebra. -
On the Convolution of Functions of Λp – Bounded Variation and a Locally
IOSR Journal of Mathematics (IOSR-JM) e-ISSN: 2278-5728,p-ISSN: 2319-765X, Volume 8, Issue 3 (Sep. - Oct. 2013), PP 70-74 www.iosrjournals.org On the Convolution of Functions of p – Bounded Variation and a Locally Compact Hausdorff Topological Group S. M. Nengem1, D. Samaila1 and M. Solomon1 1 Department of Mathematical Sciences, Adamawa State University, P. M. B. 25, Mubi, Nigeria Abstract: The smoothness-increasing operator “convolution” is well known for inheriting the best properties of each parent function. It is also well known that if f L1 and g is a Bounded Variation (BV) function, then f g inherits the properties from the parent’s spaces. This aspect of BV can be generalized in many ways and many generalizations are obtained. However, in this paper we introduce the notion of p – Bounded variation function. In relation to that we show that the convolution of two functions f g is the inverse Fourier transforms of the two functions. Moreover, we prove that if f, g BV(p)[0, 2], then f g BV(p)[0, 2], and that on any locally compact Abelian group, a version of the convolution theorem holds. Keywords: Convolution, p-Bounded variation, Fourier transform, Abelian group. I. Introduction In mathematics and, in particular, functional analysis, convolution is a mathematical operation on two functions f and g, producing a third function that is typically viewed as a modified version of one of the original functions, giving the area overlap between the two functions as a function of the amount that one of the original functions is translated.