DOCSLIB.ORG

Math 408A Convexity

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Math 408A Convexity Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Math 408A Convexity Math 408A Convexity Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity Convexity and optimality Directional Derivatives and Convexity The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Math 408A Convexity n Definition: A function f : R ! R is said to be convex if for every n two points x1; x2 2 R and λ 2 [0; 1] we have f (λx1 + (1 − λ)x2) ≤ λf (x1) + (1 − λ)f (x2): The function f is said to be strictly convex if for every two distinct n points x1; x2 2 R and λ 2 [0; 1] we have f (λx1 + (1 − λ)x2) < λf (x1) + (1 − λ)f (x2): Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity n Definition: A set C ⊂ R is said to be convex if for every x; y 2 C and λ 2 [0; 1] one has (1 − λ)x + λy 2 C : Math 408A Convexity The function f is said to be strictly convex if for every two distinct n points x1; x2 2 R and λ 2 [0; 1] we have f (λx1 + (1 − λ)x2) < λf (x1) + (1 − λ)f (x2): Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity n Definition: A set C ⊂ R is said to be convex if for every x; y 2 C and λ 2 [0; 1] one has (1 − λ)x + λy 2 C : n Definition: A function f : R ! R is said to be convex if for every n two points x1; x2 2 R and λ 2 [0; 1] we have f (λx1 + (1 − λ)x2) ≤ λf (x1) + (1 − λ)f (x2): Math 408A Convexity Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity n Definition: A set C ⊂ R is said to be convex if for every x; y 2 C and λ 2 [0; 1] one has (1 − λ)x + λy 2 C : n Definition: A function f : R ! R is said to be convex if for every n two points x1; x2 2 R and λ 2 [0; 1] we have f (λx1 + (1 − λ)x2) ≤ λf (x1) + (1 − λ)f (x2): The function f is said to be strictly convex if for every two distinct n points x1; x2 2 R and λ 2 [0; 1] we have f (λx1 + (1 − λ)x2) < λf (x1) + (1 − λ)f (x2): Math 408A Convexity (x 2 , f (x 2 )) (x 1 , f (x 1 )) x λ λ x 1 x 1 + (1 - )x 2 2 That is, the set epi (f ) = f(x; µ): f (x) ≤ µg; called the epi-graph of f , is a convex set. Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity The secant line connecting (x1; f (x1)) and (x2; f (x2)) lies above the graph of f . Math 408A Convexity (x 2 , f (x 2 )) (x 1 , f (x 1 )) x λ λ x 1 x 1 + (1 - )x 2 2 That is, the set epi (f ) = f(x; µ): f (x) ≤ µg; called the epi-graph of f , is a convex set. Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity The secant line connecting (x1; f (x1)) and (x2; f (x2)) lies above the graph of f . Math 408A Convexity (x 2 , f (x 2 )) (x 1 , f (x 1 )) x λ λ x 1 x 1 + (1 - )x 2 2 Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity The secant line connecting (x1; f (x1)) and (x2; f (x2)) lies above the graph of f . That is, the set epi (f ) = f(x; µ): f (x) ≤ µg; called the epi-graph of f , is a convex set. Math 408A Convexity Examples of convex functions are cT x; kxk; xT Hx when H is psd. Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity n Definition: A function f : R ! R [ f+1g = R¯ is said to be convex if the set epi (f ) = f(x; µ): f (x) ≤ µg is a convex set. We also define the essential domain of f to be the set dom (f ) = fx : f (x) < +1g : Math 408A Convexity Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity n Definition: A function f : R ! R [ f+1g = R¯ is said to be convex if the set epi (f ) = f(x; µ): f (x) ≤ µg is a convex set. We also define the essential domain of f to be the set dom (f ) = fx : f (x) < +1g : Examples of convex functions are cT x; kxk; xT Hx when H is psd. Math 408A Convexity Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity and Optimality n n Theorem: Let f : R ! R¯ be convex. If x 2 R is a local minimum for f , then x is a global minimum for f . If, in addition, f is strictly convex, then x¯ is the unique global minimizer. Math 408A Convexity −1 Set λ := (2kx − bxk) < 1 and xλ := x + λ(bx − x). Then kxλ − xk ≤ /2 and f (xλ) ≤ (1 − λ)f (x) + λf (bx) < f (x): This contradicts the choice of and so no such bx exists. Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity and Optimality Proof: Suppose f (bx) < f (x). Let > 0 be such that f (x) ≤ f (x) whenever kx − xk ≤ and < 2kx − bxk : Math 408A Convexity Then kxλ − xk ≤ /2 and f (xλ) ≤ (1 − λ)f (x) + λf (bx) < f (x): This contradicts the choice of and so no such bx exists. Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity and Optimality Proof: Suppose f (bx) < f (x). Let > 0 be such that f (x) ≤ f (x) whenever kx − xk ≤ and < 2kx − bxk : −1 Set λ := (2kx − bxk) < 1 and xλ := x + λ(bx − x). Math 408A Convexity This contradicts the choice of and so no such bx exists. Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity and Optimality Proof: Suppose f (bx) < f (x). Let > 0 be such that f (x) ≤ f (x) whenever kx − xk ≤ and < 2kx − bxk : −1 Set λ := (2kx − bxk) < 1 and xλ := x + λ(bx − x). Then kxλ − xk ≤ /2 and f (xλ) ≤ (1 − λ)f (x) + λf (bx) < f (x): Math 408A Convexity Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Convexity and Optimality Proof: Suppose f (bx) < f (x). Let > 0 be such that f (x) ≤ f (x) whenever kx − xk ≤ and < 2kx − bxk : −1 Set λ := (2kx − bxk) < 1 and xλ := x + λ(bx − x). Then kxλ − xk ≤ /2 and f (xλ) ≤ (1 − λ)f (x) + λf (bx) < f (x): This contradicts the choice of and so no such bx exists. Math 408A Convexity n 1. Given x; d 2 R the difference quotient f (x + td) − f (x) t is a non-decreasing function of t on (0; +1). n 0 2. For every x; d 2 R the directional derivative f (x; d) always exists and is given by f (x + td) − f (x) f 0(x; d) := inf : t>0 t Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Directional Derivatives and Convexity Directional Derivatives and Convexity n Lemma: Let f : R ! R¯ be convex (not necessarilty differentiable). Math 408A Convexity n 0 2. For every x; d 2 R the directional derivative f (x; d) always exists and is given by f (x + td) − f (x) f 0(x; d) := inf : t>0 t Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Directional Derivatives and Convexity Directional Derivatives and Convexity n Lemma: Let f : R ! R¯ be convex (not necessarilty differentiable). n 1. Given x; d 2 R the difference quotient f (x + td) − f (x) t is a non-decreasing function of t on (0; +1). Math 408A Convexity Outline Convexity Convexity and optimality The Subdifferential Inequality Convexity and Optimality Conditions Sublinear Functionals Examples of Convex Functions Directional Derivatives and Convexity Directional Derivatives and Convexity n Lemma: Let f : R ! R¯ be convex (not necessarilty differentiable). n 1. Given x; d 2 R the difference quotient f (x + td) − f (x) t is a non-decreasing function of t on (0; +1). n 0 2. For every x; d 2 R the directional derivative f (x; d) always exists and is given by f (x + td) − f (x) f 0(x; d) := inf : t>0 t Math 408A Convexity Recall that f (x + td) − f (x) f 0(x; d) := lim : t#0 t Now if the difference quotient on the right is non-decreasing in t on (0; +1), then the limit is necessarily given by the infimum of these ratios.
Load more

Recommended publications
  • The Hahn-Banach Theorem and Infima of Convex Functions
    The Hahn-Banach theorem and infima of convex functions Hajime Ishihara School of Information Science Japan Advanced Institute of Science and Technology (JAIST) Nomi, Ishikawa 923-1292, Japan first CORE meeting, LMU Munich, 7 May, 2016 Normed spaces Definition A normed space is a linear space E equipped with a norm k · k : E ! R such that I kxk = 0 $ x = 0, I kaxk = jajkxk, I kx + yk ≤ kxk + kyk, for each x; y 2 E and a 2 R. Note that a normed space E is a metric space with the metric d(x; y) = kx − yk: Definition A Banach space is a normed space which is complete with respect to the metric. Examples For 1 ≤ p < 1, let p N P1 p l = f(xn) 2 R j n=0 jxnj < 1g and define a norm by P1 p 1=p k(xn)k = ( n=0 jxnj ) : Then lp is a (separable) Banach space. Examples Classically the normed space 1 N l = f(xn) 2 R j (xn) is boundedg with the norm k(xn)k = sup jxnj n is an inseparable Banach space. However, constructively, it is not a normed space. Linear mappings Definition A mapping T between linear spaces E and F is linear if I T (ax) = aTx, I T (x + y) = Tx + Ty for each x; y 2 E and a 2 R. Definition A linear functional f on a linear space E is a linear mapping from E into R. Bounded linear mappings Definition A linear mapping T between normed spaces E and F is bounded if there exists c ≥ 0 such that kTxk ≤ ckxk for each x 2 E.
    [Show full text]
  • On Order Preserving and Order Reversing Mappings Defined on Cones of Convex Functions MANUSCRIPT
    ON ORDER PRESERVING AND ORDER REVERSING MAPPINGS DEFINED ON CONES OF CONVEX FUNCTIONS MANUSCRIPT Lixin Cheng∗ Sijie Luo School of Mathematical Sciences Yau Mathematical Sciences Center Xiamen University Tsinghua University Xiamen 361005, China Beijing 100084, China [email protected] [email protected] ABSTRACT In this paper, we first show that for a Banach space X there is a fully order reversing mapping T from conv(X) (the cone of all extended real-valued lower semicontinuous proper convex functions defined on X) onto itself if and only if X is reflexive and linearly isomorphic to its dual X∗. Then we further prove the following generalized “Artstein-Avidan-Milman” representation theorem: For every fully order reversing mapping T : conv(X) → conv(X) there exist a linear isomorphism ∗ ∗ ∗ U : X → X , x0, ϕ0 ∈ X , α> 0 and r0 ∈ R so that ∗ (Tf)(x)= α(Ff)(Ux + x0)+ hϕ0, xi + r0, ∀x ∈ X, where F : conv(X) → conv(X∗) is the Fenchel transform. Hence, these resolve two open questions. We also show several representation theorems of fully order preserving mappings de- fined on certain cones of convex functions. For example, for every fully order preserving mapping S : semn(X) → semn(X) there is a linear isomorphism U : X → X so that (Sf)(x)= f(Ux), ∀f ∈ semn(X), x ∈ X, where semn(X) is the cone of all lower semicontinuous seminorms on X. Keywords Fenchel transform · order preserving mapping · order reversing mapping · convex function · Banach space 1 Introduction An elegant theorem of Artstein-Avidan and Milman [5] states that every fully order reversing (resp.
    [Show full text]
  • Bootstrapping the Mazur–Orlicz-König Theorem and the Hahn–Banach Lagrange Theorem
    Journal of Convex Analysis Volume 25 (2018), No. 2, 1–xx Bootstrapping the Mazur–Orlicz-König Theorem and the Hahn–Banach Lagrange Theorem Stephen Simons Department of Mathematics, University of California, Santa Barbara, CA 93106-3080, U.S.A. [email protected] Dedicated to Antonino Maugeri on the occasion of his 70th birthday. Received: December 25, 2015 Accepted: August 4, 2016 We give some extensions of König’s extension of the Mazur–Orlicz theorem. These extensions include generalizations of a surprising recent result of Sun Chuanfeng, and generalizations to the product of more than two spaces of the “Hahn–Banach–Lagrange” theorem. Keywords: Sublinear functional, convex function, affine function, Hahn–Banach theorem, Mazur–Orlicz–König theorem. 2010 Mathematics Subject Classification: 46A22, 46N10. 1. Introduction In this paper, all vector spaces are real. We shall use the terms sublinear, linear, convex, concave and affine in their usual senses. This paper is about extensions of the Mazur–Orlicz theorem, which first ap- peared in [5]: Let E be a vector space, S : E ! R be sublinear and C be a nonempty convex subset of E. Then there exists a linear map L: E ! R such that L ≤ S on E and infC L = infC S: Early improvements and applications of this result were given, in chronological order, by Sikorski [6], Pták [4], König [1], Landsberg–Schirotzek [3] and König [2]. By a convex–affine version of a known result we mean that it corresponds to the known result with the word sublinear in the hypothesis replaced by convex and the word linear in the conclusion replaced by affine.
    [Show full text]
  • Constructing Sublinear Expectations on Path Space
    Constructing Sublinear Expectations on Path Space Marcel Nutz∗ Ramon van Handel† January 21, 2013 Abstract We provide a general construction of time-consistent sublinear ex- pectations on the space of continuous paths. It yields the existence of the conditional G-expectation of a Borel-measurable (rather than quasi-continuous) random variable, a generalization of the random G- expectation, and an optional sampling theorem that holds without exceptional set. Our results also shed light on the inherent limitations to constructing sublinear expectations through aggregation. Keywords Sublinear expectation; G-expectation; random G-expectation; Time- consistency; Optional sampling; Dynamic programming; Analytic set AMS 2000 Subject Classification 93E20; 60H30; 91B30; 28A05 1 Introduction d We study sublinear expectations on the space Ω= C0(R+, R ) of continuous arXiv:1205.2415v3 [math.PR] 11 Apr 2013 paths. Taking the dual point of view, we are interested in mappings P ξ 0(ξ)= sup E [ξ], 7→ E P ∈P where ξ is a random variable and is a set of probability measures, possibly P non-dominated. In fact, any sublinear expectation with certain continuity ∗Dept. of Mathematics, Columbia University, New York. [email protected] The work of MN is partially supported by NSF grant DMS-1208985. †Sherrerd Hall rm. 227, Princeton University, Princeton. [email protected] The work of RvH is partially supported by NSF grant DMS-1005575. 1 properties is of this form (cf. [10, Sect. 4]). Under appropriate assumptions on , we would like to construct a conditional expectation (ξ) at any P Eτ stopping time τ of the the filtration generated by the canonical process {Ft} B and establish the tower property ( (ξ)) = (ξ) for stopping times σ τ, (1.1) Eσ Eτ Eσ ≤ a property also known as time-consistency in this context.
    [Show full text]
  • The Constructive Hahn-Banach Theorem, Revisited
    The constructive Hahn-Banach theorem, revisited Hajime Ishihara School of Information Science Japan Advanced Institute of Science and Technology (JAIST) Nomi, Ishikawa 923-1292, Japan Workshop: Constructive Mathematics HIM Bonn, 6{10 August, 2018 The Hahn-Banach theorem The Hahn-Banach theorem is a central tool in functional analysis, which is named after the mathematicians Hans Hahn and Stefan Banach who proved it independently in the late 1920s. The theorem has several forms. I The continuous extension theorem which allows the extension of a bounded linear functional defined on a subspace of a normed space to the whole space; I The separation theorem which allows a separation of two convex subsets of a normed space by a hyperplane, and has numerous uses in convex analysis (geometry); I The dominated extension theorem which is the most general form of the theorem, and allows the extension of a linear functional defined on a subspace of a normed space, and dominated by a sublinear function, to the whole space. Normed spaces Definition 1 A normed space is a linear space E equipped with a norm k · k : E ! R such that I kxk = 0 $ x = 0, I kaxk = jajkxk, I kx + yk ≤ kxk + kyk, for each x; y 2 E and a 2 R. Note that a normed space E is a metric space with the metric d(x; y) = kx − yk: Definition 2 A Banach space is a normed space which is complete with respect to the metric. Examples For 1 ≤ p < 1, let N P1 p lp = f(xn) 2 R j n=0 jxnj < 1g and define a norm by P1 p 1=p k(xn)k = ( n=0 jxnj ) : Then lp is a (separable) Banach space.
    [Show full text]
  • Multi-Dimensional Central Limit Theorems and Laws of Large
    Multi-dimensional central limit theorems and laws of large numbers under sublinear expectations Ze-Chun Hu∗ and Ling Zhou Nanjing University Abstract In this paper, we present some multi-dimensional central limit theorems and laws of large numbers under sublinear expectations, which extend some previous results. Keywords central limit theorem, laws of large numbers, sublinear expectation MSC(2000): 60H10, 60H05 1 Introduction The classic central limit theorems and strong (weak) laws of large numbers play an important role in the development of probability theory and its applications. Recently, motivated by the risk measures, superhedge pricing and modeling uncertain in finance, Peng [1, 2, 3, 4, 5, 6] initiated the notion of independent and identically distributed (IID) random variables under sublinear expectations, and proved the central limit theorems and the weak law of large numbers among other things. In [7], Hu and Zhang obtained a central limit theorem for capacities induced by sublinear expectations. In [8], Li and Shi proved a central limit theorem without the requirement of identical distribution, which extends Peng’s central limit theorems in [2, 4]. In [9], Hu proved that for any arXiv:1211.1090v2 [math.PR] 19 Jun 2013 continuous function ψ satisfying the growth condition ψ(x) C(1+ x p) for some C > 0,p 1 depending on ψ, central limit theorem under sublinear| expectations| ≤ | obtained| by Peng [4] still≥ holds. In [10], Zhang obtained a central limit theorem for weighted sums of independent random variables under sublinear expectations, which extends the results obtained by Peng, Li and Shi. Now we recall the existing results about the laws of large numbers under sublinear expec- tations.
    [Show full text]
  • Notes, We Review Useful Concepts in Convexity and Normed Spaces
    Mastermath, Spring 2019 Lecturers: D. Dadush, J. Briet Geometric Functional Analysis Lecture 0 Scribe: D. Dadush Review of Convexity and Normed Spaces In these notes, we review useful concepts in convexity and normed spaces. As a warning, many of the statements made here will not be proved. For detailed proofs, one is recommended to look up reference books in functional and convex analysis. The intent here is for these notes to serve as a reference for the basic language for the course. The reader may thus skim the material upon first reading, and then reread more carefully later during the course if the relevant concepts remain unclear. 1 Basic Convexity In this section, we will focus on convexity theorems in a finite dimensional real vector space, which will identify with Rn or subspaces theoreof. For vectors x, y 2 Rn, we define the standard T n n inner product hx, yi := x y = ∑i=1 xiyi. For two sets A, B ⊆ R and scalars c, d 2 R, we define the Minkowski sum cA + dB = fca + db : a 2 A, b 2 Bg. A convex set K ⊆ Rn is a set for which the line segment between any two points is in the set, or formally for which 8x, y 2 K, l 2 [0, 1], lx + (1 − l)y 2 K. A useful equivalent definition is that a non-empty set K is convex if 8a, b ≥ 0, aK + bK = (a + b)K. If K = −K, K is said to be symmetric. If K is compact with non-empty interior, then K is a convex body.
    [Show full text]
  • Topological Vector Spaces
    An introduction to some aspects of functional analysis, 3: Topological vector spaces Stephen Semmes Rice University Abstract In these notes, we give an overview of some aspects of topological vector spaces, including the use of nets and filters. Contents 1 Basic notions 3 2 Translations and dilations 4 3 Separation conditions 4 4 Bounded sets 6 5 Norms 7 6 Lp Spaces 8 7 Balanced sets 10 8 The absorbing property 11 9 Seminorms 11 10 An example 13 11 Local convexity 13 12 Metrizability 14 13 Continuous linear functionals 16 14 The Hahn–Banach theorem 17 15 Weak topologies 18 1 16 The weak∗ topology 19 17 Weak∗ compactness 21 18 Uniform boundedness 22 19 Totally bounded sets 23 20 Another example 25 21 Variations 27 22 Continuous functions 28 23 Nets 29 24 Cauchy nets 30 25 Completeness 32 26 Filters 34 27 Cauchy filters 35 28 Compactness 37 29 Ultrafilters 38 30 A technical point 40 31 Dual spaces 42 32 Bounded linear mappings 43 33 Bounded linear functionals 44 34 The dual norm 45 35 The second dual 46 36 Bounded sequences 47 37 Continuous extensions 48 38 Sublinear functions 49 39 Hahn–Banach, revisited 50 40 Convex cones 51 2 References 52 1 Basic notions Let V be a vector space over the real or complex numbers, and suppose that V is also equipped with a topological structure. In order for V to be a topological vector space, we ask that the topological and vector spaces structures on V be compatible with each other, in the sense that the vector space operations be continuous mappings.
    [Show full text]
  • MATHEMATICS 116, FALL 2015 REAL ANALYSIS, CONVEXITY and OPTIMIZATION Proof List for the Fnall Exam Last Modified
    MATHEMATICS 116, FALL 2015 REAL ANALYSIS, CONVEXITY AND OPTIMIZATION Proof List for the fnall exam Last modified: December 3, 2015 The final exam will have one proof chosen at random from 1-3, one from 4-6, one from 7-8, and one from 9-10. In some cases the proof listed here is only part of a longer proof that was presented in class, or I have made simplifying assumptions such as assuming a complete normed space with a closed subspace. 1. Let M be a closed subspace of a normed vector space X, let p(x) be a continuous sublinear functional defined on all of X, and let f(m) be a linear functional defined on M that satisfies f(m) ≤ p(m) for all m 2 M. Let y be a vector in X that is not in M. Prove that, by choosing an appropriate value for g(y); it is possible to define a linear functional g on the subspace [M + y] such that g(m) = f(m) for all m 2 M and g(x) ≤ p(x) for all x 2 [M + y]. Remember to consider vectors of the form x = m + αy; α > 0 and vectors of the form x = m − βy; β > 0: 2. Let K be a convex set with nonempty interior in a vector space X. Assume that θ is in the interior of K. Suppose that V is a linear variety in X containing no interior points of K. Using the extension form of the Hahn-Banach theorem, prove that there exists an element x∗ 2 X∗ such that < v; x∗ >= 1 for all v 2 V .
    [Show full text]
  • Real Analysis
    JUHA KINNUNEN Real Analysis Department of Mathematics and Systems Analysis, Aalto University 2020 Contents 1 Lp spaces1 1.1 Lp functions ................................. 1 1.2 Lp norm .................................... 4 1.3 Lp spaces for 0 p 1 ............................ 12 Ç Ç 1.4 Completeness of Lp ............................. 13 1.5 L1 space ................................... 19 2 The Hardy-Littlewood maximal function 25 2.1 Local Lp spaces ............................... 25 2.2 Definition of the maximal function .................... 27 2.3 Hardy-Littlewood-Wiener maximal function theorems ........ 29 2.4 The Lebesgue differentiation theorem .................. 37 2.5 The fundamental theorem of calculus .................. 43 2.6 Points of density ............................... 44 2.7 The Sobolev embedding .......................... 47 3 Convolutions 52 3.1 Two additional properties of Lp ...................... 52 3.2 Convolution .................................. 56 3.3 Approximations of the identity ...................... 61 3.4 Pointwise convergence ........................... 64 3.5 Convergence in Lp ............................. 68 3.6 Smoothing properties ............................ 70 3.7 The Poisson kernel .............................. 73 4 Differentiation of measures 75 4.1 Covering theorems ............................. 75 4.2 The Lebesgue differentiation theorem for Radon measures ..... 84 4.3 The Radon-Nikodym theorem ....................... 89 4.4 The Lebesgue decomposition ....................... 92 4.5 Lebesgue
    [Show full text]
  • On a Conic Approach to Convex Analysis 1 Introduction
    On a Conic Approach to Convex Analysis Jan Brinkhuis February 2008 Econometric Institute Report EI 2008-05 Abstract The aim of this paper is to make an attempt to justify the main results from Convex Analysis by one elegant tool, the conification method, which consists of three steps: conify, work with convex cones, deconify. It is based on the fact that the standard operations and facts (‘the calculi’) are much simpler for special convex sets, convex cones. By considering suitable classes of convex cones, we get the standard operations and facts for all situations in the complete generality that is required. The main advantages of this conification method are that the standard operations— linear image, inverse linear image, closure, the duality operator, the binary operations and the inf- operator—are defined on all objects of each class of convex objects—convex sets, convex functions, convex cones and sublinear functions—and that moreover the standard facts—such as the duality theorem—hold forall closed convex objects. This requires that the analysis is carried out in the context of convex objects over cosmic space, the space that is obtained from ordinary space by adding a horizon, representing the directions of ordinary space. 1 Introduction Reduction to conic calculus. This paper presents some progress on joint work with V.M.Tikhomirov that was published in [7]. It is an attempt to develop a general and universal tool for justifying the main results of Convex Analysis. The idea is as follows. Many standard operations and facts are much simpler and more complete for special convex objects, convex cones.
    [Show full text]
  • Norms, the Dual, Continuity
    Norms and Continuity TOC Eval Convexity H-B Norms Continuity Eval Redux Bases Adjoint Norms and Continuity TOC Eval Convexity H-B Norms Continuity Eval Redux Bases Adjoint Norms, the Dual, Continuity Table of Contents Continuity The Evaluation Map The Evaluation Map Revisited Larry Susanka Convexity Schauder Bases in a Banach The Hahn-Banach Theorem Space Mathematics Program Norms The Banach Adjoint Bellevue College June 13, 2013 Norms and Continuity TOC Eval Convexity H-B Norms Continuity Eval Redux Bases Adjoint Norms and Continuity TOC Eval Convexity H-B Norms Continuity Eval Redux Bases Adjoint THE EVALUATION MAP THE EVALUATION MAP E is linear, and also one-to-one: We suppose V is a vector space and V∗ is its dual. that is, E(x) = E(y) exactly when x = y. V∗ is, itself, a vector space so it too has a dual, V∗∗ = (V∗)∗. There is an obvious collection of members of V∗∗, namely So if E is onto then it is invertible and an isomorphism. evaluation of a functional at members of V. In that case, (V∗)∗ can be identified with (i.e. it is) V. ∗ ∗ Define the evaluation map E: V ! (V ) by If V is finite dimensional, V and V∗ have the same dimension. And it follows that (V∗)∗ has the same dimension as does V. So E(x)(f ) = f (x) for each x 2 V and f 2 V∗: E must be onto and therefore an isomorphism. The infinite dimensional case is much more delicate and we will consider the extent to which we can recover this important identification in some form later.
    [Show full text]