j=1 [We will show that the triangle inequality holds for each p-norm in Chapter 3 Section 6.] The 1-norm is A F = tr(a H A).
|
|
- Howard Page
- 5 years ago
- Views:
Transcription
1 Math 344 Lecture # Normed Linear Spaces Definition A seminorm on a vector space V over F is a map : V R that for all x, y V and for all α F satisfies (i) x 0 (positivity), (ii) αx = α x (scale preservation) (iii) x + y x + y (triangle inequality) A norm on V is a seminorm that satisfies the property of x = 0 if and only if x = 0. A vector space V with a norm is called a normed linear space (NLS) and is denoted by (V, ). Theorem Every inner product space (V,, ) is a normed linear space with the norm x = x, x. See the Appendix for a proof Examples Examples and Let x = [ x 1 x 2 x n ] T F n. For p [1, ) the p-norm on F n is ( ) 1/p x p = x j p. [We will show that the triangle inequality holds for each p-norm in Chapter 3 Section 6.] The 1-norm is The 2-norm, x 1 = x 1 + x x n. x 2 = x x x n 2, is that obtained by the standard inner product on F n. The -norm (i.e., p = ), x = sup{ x 1, x 2,..., x n }, is the limit of x p as p. Example The Frobenius norm on M m n (F) is given by A F = tr(a H A). This norm is invariant under left multiplication of orthonormal m m matrices Q because (QA) H (QA) = A H Q H QA = A H IA = A H A.
2 Example For p [1, ), the p-norm on L p ([a, b], F) is The -norm on L ([a, b], F) is ( b 1/p f p = f(x) dx) p. a f = sup f(x). x [a,b] Defintion For a normed linear space Y with norm Y and a nonempty set X, define the L -norm of f : X Y by f L = sup f(x) Y. x X Let L (X; Y ) be the collection of all f : X Y for which f L <. Proposition For a normed linear space Y and any nonempty set X, the pair (L (X; Y ), L ) is a normed linear space. The proof of this is HW (Exercise 3.25) Induced Norms on Linear Transformations Definition Let (V, V ) and (W, W ) be two normed linear spaces. The norm of T L (V, W ) is defined to be quantity T x W T V,W = sup. x 0 induced by the norms on V and W. A map T L (V, W ) is called bounded if T V,W <. Let B(V, W ) denote the collection of all bounded T L (V, W ). If W = V, we write B(V ) instead of B(V, V ) and write (by abuse of notation) T V instead of T V,V. The set B(V ) is the collection of all bounded T L (V ) and V is the operator norm. Equivalent Definitions of T V,W. A simple proof (that the book does not give) for is: for nonzero y V set α = y V T x W sup = sup T x W. x 0 =1 and x = α 1 y, so that = 1 and y = αx; then T y W y V = T (αx) W αx V = α T x W α = T x W = T x W, so the supremum over y 0 is the same as the supremum over x with = 1. It is also true that sup =1 T x W = sup 1 T x W. Theorem The collection B(V, W ) is a subspace of L (V, W ) and the pair (B(V, W ), V,W ) is a normed linear space.
3 See the Appendix for a proof. Remark For each T B(V, W ), the norm V,W satisfies because for nonzero x V, we have ( ) T x W = T x W T x W T V,W for all x V, ( ) x W = T T V,W. In fact, the quantity T V,W is the smallest one for which T x W T V,W for all x V. Remark When V and W are finite dimensional normed linear spaces, we have B(V, W ) is precisely L (V, W ). This is generally not true when V and W are infinite dimensional. Theorem Let (V, V ), (W, W ), and (X, X ) be normed linear spaces. If T B(V, W ) and S B(W, X), then ST B(V, X) and ST V,X S W,X T V,W. In particular, the operator norm V on B(V ) satisfies the submultiplicative property ST V S V T V for all S, T B(V ). Proof. For v V we have giving the result. ST v X = S(T v) X S W,X T v W S W,X T V,W v V, Definition A norm on M n (F) is called a matrix norm if AB A B for all A, B M n (F) (i.e., it satisfies the submultiplicative property). Example For 1 p, the p-norms on F m and F n induce a norm p on M m n (F) defined by Ax p A p = sup. x 0 x p When m = n, the norm p is the induced operator norm on M n (F). Theorem shows that this induced operator norm p is submultiplicative, and so p is a matrix norm. Unexample Although not an induced norm, the Frobenius norm F M n (F) is a matrix norm, as to be shown in HW (Exercise 4.28) Explicit Formulas for A 1 and A on
4 Theorem For A = [a ij ] M m n (F) we have See the Appendix for a proof. A 1 = sup 1 j n A = sup 1 i n m a ij, i=1 a ij. i=j
5 Appendix Proof of Theorem We have already shown in Remark that x = x, x satisfies properties (i) and (ii) and that x = 0 if and only if x = 0. To show property (iii) holds, we have x + y 2 = x + y, x + y = x, x + x, y + y, x + y, y x x, y + y 2 x x y + y 2 = ( x + y ) 2, where for the first inequality, x, y + y, x = x, y + x, y = a + ib + a ib = 2a is a real number bounded above by 2 x, y = 2 a 2 + b 2, and for the second inequality, we used the Cauchy-Schwarz inequality. Proof of Theorem First we show that the induced norm V,W is indeed a norm on B(V, W ). (i) Positivity T V,W 0 and T V,W = 0 if and only if T = 0. That T V,W 0 follows directly from the definition of the induced norm. Now if T = 0 (the zero transformation T x = 0 for all x V ), then T x W = 0 for all x V, so that T V,W = 0. We use the contrapositive to show that T V,W = 0 implies T = 0. Suppose there is a nonzero y V such that T y 0. Then so that T V,W > 0. T x W sup x 0 T y V y V > 0 (ii) Scale Preservation. For T B(V, W ) and α F we have at V,W = sup at (x) W = sup α T x W = α sup T x W. =1 =1 =1 (iii) Triangle Inequality. For S, T B(V, W ), we have S + T V,W = sup (S + T )x W =1 = sup Sx + T x W =1 ( ) sup Sx W + T x W =1 sup Sx W + sup T x W =1 =1 = S V,W + T V,W, where for the first inequality we have used the triangle inequality for W, and for the second inequality we have used the following property of supremum.
6 For α = sup x V =1( Sx W + T x W ), β = sup x V =1 Sx W, and γ = sup x V =1 T x W, there is for ɛ > 0 a y V satisfying y V = 1 such that α ɛ < Sy W + T y W β + γ. This holds for any ɛ > 0 which implies that α β + γ. We have shown that V,W is an norm on B(V, W ). Scale preservation shows that B(V, W ) is closed under scalar multiplication, and the triangle inequality shows that B(V, W ) is closed under addition. Thus the subset B(V, W ) of L (V, W ) is a subspace of L (V, W ), and hence B(V, W ) is a normed linear space with the norm V,W. Proof of Theorem The proof of the formula for A 1 is HW (Exercise 3.27). Here is a proof of the formula for A. Writing x = [ x 1 x 2 x n ] T F n, the i th entry of Ax F n is a ij x j. We thus we obtain Ax = sup a i i m ij x j sup a ij x j sup When x 0, we can divide both sides by the positive x to get Ax x sup We now show the opposite inequality holds too. Let k be the row index satisfying a kj = sup a ij. a ij. a ij x. Let x F n be the vector whose i th entry is 0 if a ki = 0, and is a ki / a ki if a ki 0. If every entry of x were zero, then a ki = 0 for all i = 1,..., m, and since n a kj = sup n a ij, every entry of A would be zero, in which case the formula holds. So we may assume that x 0, which implies that x = 1. From Ax A x we have A Ax a kj = sup a ij x a kj a kj a kj a kj = because of the meaning of k. a jk 2 a jk = sup a ij
Section 3.9. Matrix Norm
3.9. Matrix Norm 1 Section 3.9. Matrix Norm Note. We define several matrix norms, some similar to vector norms and some reflecting how multiplication by a matrix affects the norm of a vector. We use matrix
More informationFunctional Analysis Review
Outline 9.520: Statistical Learning Theory and Applications February 8, 2010 Outline 1 2 3 4 Vector Space Outline A vector space is a set V with binary operations +: V V V and : R V V such that for all
More informationPart 1a: Inner product, Orthogonality, Vector/Matrix norm
Part 1a: Inner product, Orthogonality, Vector/Matrix norm September 19, 2018 Numerical Linear Algebra Part 1a September 19, 2018 1 / 16 1. Inner product on a linear space V over the number field F A map,
More informationLecture # 3 Orthogonal Matrices and Matrix Norms. We repeat the definition an orthogonal set and orthornormal set.
Lecture # 3 Orthogonal Matrices and Matrix Norms We repeat the definition an orthogonal set and orthornormal set. Definition A set of k vectors {u, u 2,..., u k }, where each u i R n, is said to be an
More informationLecture 5. Ch. 5, Norms for vectors and matrices. Norms for vectors and matrices Why?
KTH ROYAL INSTITUTE OF TECHNOLOGY Norms for vectors and matrices Why? Lecture 5 Ch. 5, Norms for vectors and matrices Emil Björnson/Magnus Jansson/Mats Bengtsson April 27, 2016 Problem: Measure size of
More informationReview of Basic Concepts in Linear Algebra
Review of Basic Concepts in Linear Algebra Grady B Wright Department of Mathematics Boise State University September 7, 2017 Math 565 Linear Algebra Review September 7, 2017 1 / 40 Numerical Linear Algebra
More informationLinear Algebra Massoud Malek
CSUEB Linear Algebra Massoud Malek Inner Product and Normed Space In all that follows, the n n identity matrix is denoted by I n, the n n zero matrix by Z n, and the zero vector by θ n An inner product
More informationSection 7.5 Inner Product Spaces
Section 7.5 Inner Product Spaces With the dot product defined in Chapter 6, we were able to study the following properties of vectors in R n. ) Length or norm of a vector u. ( u = p u u ) 2) Distance of
More informationMath 290, Midterm II-key
Math 290, Midterm II-key Name (Print): (first) Signature: (last) The following rules apply: There are a total of 20 points on this 50 minutes exam. This contains 7 pages (including this cover page) and
More informationBasic Concepts in Linear Algebra
Basic Concepts in Linear Algebra Grady B Wright Department of Mathematics Boise State University February 2, 2015 Grady B Wright Linear Algebra Basics February 2, 2015 1 / 39 Numerical Linear Algebra Linear
More informationKernel Method: Data Analysis with Positive Definite Kernels
Kernel Method: Data Analysis with Positive Definite Kernels 2. Positive Definite Kernel and Reproducing Kernel Hilbert Space Kenji Fukumizu The Institute of Statistical Mathematics. Graduate University
More informationDS-GA 1002 Lecture notes 0 Fall Linear Algebra. These notes provide a review of basic concepts in linear algebra.
DS-GA 1002 Lecture notes 0 Fall 2016 Linear Algebra These notes provide a review of basic concepts in linear algebra. 1 Vector spaces You are no doubt familiar with vectors in R 2 or R 3, i.e. [ ] 1.1
More informationReview of Some Concepts from Linear Algebra: Part 2
Review of Some Concepts from Linear Algebra: Part 2 Department of Mathematics Boise State University January 16, 2019 Math 566 Linear Algebra Review: Part 2 January 16, 2019 1 / 22 Vector spaces A set
More informationCharacterisation of Accumulation Points. Convergence in Metric Spaces. Characterisation of Closed Sets. Characterisation of Closed Sets
Convergence in Metric Spaces Functional Analysis Lecture 3: Convergence and Continuity in Metric Spaces Bengt Ove Turesson September 4, 2016 Suppose that (X, d) is a metric space. A sequence (x n ) X is
More informationLinear Analysis Lecture 5
Linear Analysis Lecture 5 Inner Products and V Let dim V < with inner product,. Choose a basis B and let v, w V have coordinates in F n given by x 1. x n and y 1. y n, respectively. Let A F n n be the
More informationb 1 b 2.. b = b m A = [a 1,a 2,...,a n ] where a 1,j a 2,j a j = a m,j Let A R m n and x 1 x 2 x = x n
Lectures -2: Linear Algebra Background Almost all linear and nonlinear problems in scientific computation require the use of linear algebra These lectures review basic concepts in a way that has proven
More informationNORMS ON SPACE OF MATRICES
NORMS ON SPACE OF MATRICES. Operator Norms on Space of linear maps Let A be an n n real matrix and x 0 be a vector in R n. We would like to use the Picard iteration method to solve for the following system
More informationFunctional Analysis MATH and MATH M6202
Functional Analysis MATH 36202 and MATH M6202 1 Inner Product Spaces and Normed Spaces Inner Product Spaces Functional analysis involves studying vector spaces where we additionally have the notion of
More informationMathematics Department Stanford University Math 61CM/DM Inner products
Mathematics Department Stanford University Math 61CM/DM Inner products Recall the definition of an inner product space; see Appendix A.8 of the textbook. Definition 1 An inner product space V is a vector
More informationElementary linear algebra
Chapter 1 Elementary linear algebra 1.1 Vector spaces Vector spaces owe their importance to the fact that so many models arising in the solutions of specific problems turn out to be vector spaces. The
More informationBasic Calculus Review
Basic Calculus Review Lorenzo Rosasco ISML Mod. 2 - Machine Learning Vector Spaces Functionals and Operators (Matrices) Vector Space A vector space is a set V with binary operations +: V V V and : R V
More informationInner products and Norms. Inner product of 2 vectors. Inner product of 2 vectors x and y in R n : x 1 y 1 + x 2 y x n y n in R n
Inner products and Norms Inner product of 2 vectors Inner product of 2 vectors x and y in R n : x 1 y 1 + x 2 y 2 + + x n y n in R n Notation: (x, y) or y T x For complex vectors (x, y) = x 1 ȳ 1 + x 2
More informationINNER PRODUCT SPACE. Definition 1
INNER PRODUCT SPACE Definition 1 Suppose u, v and w are all vectors in vector space V and c is any scalar. An inner product space on the vectors space V is a function that associates with each pair of
More informationContents. Appendix D (Inner Product Spaces) W-51. Index W-63
Contents Appendix D (Inner Product Spaces W-5 Index W-63 Inner city space W-49 W-5 Chapter : Appendix D Inner Product Spaces The inner product, taken of any two vectors in an arbitrary vector space, generalizes
More informationFunctional Analysis Review
Functional Analysis Review Lorenzo Rosasco slides courtesy of Andre Wibisono 9.520: Statistical Learning Theory and Applications September 9, 2013 1 2 3 4 Vector Space A vector space is a set V with binary
More informationSPECTRAL THEOREM FOR COMPACT SELF-ADJOINT OPERATORS
SPECTRAL THEOREM FOR COMPACT SELF-ADJOINT OPERATORS G. RAMESH Contents Introduction 1 1. Bounded Operators 1 1.3. Examples 3 2. Compact Operators 5 2.1. Properties 6 3. The Spectral Theorem 9 3.3. Self-adjoint
More informationTypical Problem: Compute.
Math 2040 Chapter 6 Orhtogonality and Least Squares 6.1 and some of 6.7: Inner Product, Length and Orthogonality. Definition: If x, y R n, then x y = x 1 y 1 +... + x n y n is the dot product of x and
More informationMath 328 Course Notes
Math 328 Course Notes Ian Robertson March 3, 2006 3 Properties of C[0, 1]: Sup-norm and Completeness In this chapter we are going to examine the vector space of all continuous functions defined on the
More informationLinear Algebra. Session 12
Linear Algebra. Session 12 Dr. Marco A Roque Sol 08/01/2017 Example 12.1 Find the constant function that is the least squares fit to the following data x 0 1 2 3 f(x) 1 0 1 2 Solution c = 1 c = 0 f (x)
More information1. General Vector Spaces
1.1. Vector space axioms. 1. General Vector Spaces Definition 1.1. Let V be a nonempty set of objects on which the operations of addition and scalar multiplication are defined. By addition we mean a rule
More informationFunctional Analysis Exercise Class
Functional Analysis Exercise Class Week: December 4 8 Deadline to hand in the homework: your exercise class on week January 5. Exercises with solutions ) Let H, K be Hilbert spaces, and A : H K be a linear
More informationExercise Sheet 1.
Exercise Sheet 1 You can download my lecture and exercise sheets at the address http://sami.hust.edu.vn/giang-vien/?name=huynt 1) Let A, B be sets. What does the statement "A is not a subset of B " mean?
More informationVector Spaces. Commutativity of +: u + v = v + u, u, v, V ; Associativity of +: u + (v + w) = (u + v) + w, u, v, w V ;
Vector Spaces A vector space is defined as a set V over a (scalar) field F, together with two binary operations, i.e., vector addition (+) and scalar multiplication ( ), satisfying the following axioms:
More informationNotions such as convergent sequence and Cauchy sequence make sense for any metric space. Convergent Sequences are Cauchy
Banach Spaces These notes provide an introduction to Banach spaces, which are complete normed vector spaces. For the purposes of these notes, all vector spaces are assumed to be over the real numbers.
More informationLecture 1: Review of linear algebra
Lecture 1: Review of linear algebra Linear functions and linearization Inverse matrix, least-squares and least-norm solutions Subspaces, basis, and dimension Change of basis and similarity transformations
More informationLinear Algebra Review
January 29, 2013 Table of contents Metrics Metric Given a space X, then d : X X R + 0 and z in X if: d(x, y) = 0 is equivalent to x = y d(x, y) = d(y, x) d(x, y) d(x, z) + d(z, y) is a metric is for all
More informationDefinition 1. A set V is a vector space over the scalar field F {R, C} iff. there are two operations defined on V, called vector addition
6 Vector Spaces with Inned Product Basis and Dimension Section Objective(s): Vector Spaces and Subspaces Linear (In)dependence Basis and Dimension Inner Product 6 Vector Spaces and Subspaces Definition
More informationRecall: Dot product on R 2 : u v = (u 1, u 2 ) (v 1, v 2 ) = u 1 v 1 + u 2 v 2, u u = u u 2 2 = u 2. Geometric Meaning:
Recall: Dot product on R 2 : u v = (u 1, u 2 ) (v 1, v 2 ) = u 1 v 1 + u 2 v 2, u u = u 2 1 + u 2 2 = u 2. Geometric Meaning: u v = u v cos θ. u θ v 1 Reason: The opposite side is given by u v. u v 2 =
More informationYour first day at work MATH 806 (Fall 2015)
Your first day at work MATH 806 (Fall 2015) 1. Let X be a set (with no particular algebraic structure). A function d : X X R is called a metric on X (and then X is called a metric space) when d satisfies
More informationDefinitions and Properties of R N
Definitions and Properties of R N R N as a set As a set R n is simply the set of all ordered n-tuples (x 1,, x N ), called vectors. We usually denote the vector (x 1,, x N ), (y 1,, y N ), by x, y, or
More informationChapter 3. Vector spaces
Chapter 3. Vector spaces Lecture notes for MA1111 P. Karageorgis pete@maths.tcd.ie 1/22 Linear combinations Suppose that v 1,v 2,...,v n and v are vectors in R m. Definition 3.1 Linear combination We say
More informationSpectral Theory, with an Introduction to Operator Means. William L. Green
Spectral Theory, with an Introduction to Operator Means William L. Green January 30, 2008 Contents Introduction............................... 1 Hilbert Space.............................. 4 Linear Maps
More informationL p Functions. Given a measure space (X, µ) and a real number p [1, ), recall that the L p -norm of a measurable function f : X R is defined by
L p Functions Given a measure space (, µ) and a real number p [, ), recall that the L p -norm of a measurable function f : R is defined by f p = ( ) /p f p dµ Note that the L p -norm of a function f may
More informationy 2 . = x 1y 1 + x 2 y x + + x n y n 2 7 = 1(2) + 3(7) 5(4) = 3. x x = x x x2 n.
6.. Length, Angle, and Orthogonality In this section, we discuss the defintion of length and angle for vectors and define what it means for two vectors to be orthogonal. Then, we see that linear systems
More informationMATH 51H Section 4. October 16, Recall what it means for a function between metric spaces to be continuous:
MATH 51H Section 4 October 16, 2015 1 Continuity Recall what it means for a function between metric spaces to be continuous: Definition. Let (X, d X ), (Y, d Y ) be metric spaces. A function f : X Y is
More informationM17 MAT25-21 HOMEWORK 6
M17 MAT25-21 HOMEWORK 6 DUE 10:00AM WEDNESDAY SEPTEMBER 13TH 1. To Hand In Double Series. The exercises in this section will guide you to complete the proof of the following theorem: Theorem 1: Absolute
More information4 Linear operators and linear functionals
4 Linear operators and linear functionals The next section is devoted to studying linear operators between normed spaces. Definition 4.1. Let V and W be normed spaces over a field F. We say that T : V
More informationFunctional Analysis Exercise Class
Functional Analysis Exercise Class Week 9 November 13 November Deadline to hand in the homeworks: your exercise class on week 16 November 20 November Exercises (1) Show that if T B(X, Y ) and S B(Y, Z)
More informationMATH 304 Linear Algebra Lecture 10: Linear independence. Wronskian.
MATH 304 Linear Algebra Lecture 10: Linear independence. Wronskian. Spanning set Let S be a subset of a vector space V. Definition. The span of the set S is the smallest subspace W V that contains S. If
More informationAlgebra II. Paulius Drungilas and Jonas Jankauskas
Algebra II Paulius Drungilas and Jonas Jankauskas Contents 1. Quadratic forms 3 What is quadratic form? 3 Change of variables. 3 Equivalence of quadratic forms. 4 Canonical form. 4 Normal form. 7 Positive
More informationMathematical Methods wk 1: Vectors
Mathematical Methods wk : Vectors John Magorrian, magog@thphysoxacuk These are work-in-progress notes for the second-year course on mathematical methods The most up-to-date version is available from http://www-thphysphysicsoxacuk/people/johnmagorrian/mm
More informationMathematical Methods wk 1: Vectors
Mathematical Methods wk : Vectors John Magorrian, magog@thphysoxacuk These are work-in-progress notes for the second-year course on mathematical methods The most up-to-date version is available from http://www-thphysphysicsoxacuk/people/johnmagorrian/mm
More informationQuantum Computing Lecture 2. Review of Linear Algebra
Quantum Computing Lecture 2 Review of Linear Algebra Maris Ozols Linear algebra States of a quantum system form a vector space and their transformations are described by linear operators Vector spaces
More informationInner products. Theorem (basic properties): Given vectors u, v, w in an inner product space V, and a scalar k, the following properties hold:
Inner products Definition: An inner product on a real vector space V is an operation (function) that assigns to each pair of vectors ( u, v) in V a scalar u, v satisfying the following axioms: 1. u, v
More informationLecture 8 : Eigenvalues and Eigenvectors
CPS290: Algorithmic Foundations of Data Science February 24, 2017 Lecture 8 : Eigenvalues and Eigenvectors Lecturer: Kamesh Munagala Scribe: Kamesh Munagala Hermitian Matrices It is simpler to begin with
More informationLecture 20: 6.1 Inner Products
Lecture 0: 6.1 Inner Products Wei-Ta Chu 011/1/5 Definition An inner product on a real vector space V is a function that associates a real number u, v with each pair of vectors u and v in V in such a way
More informationClass notes: Approximation
Class notes: Approximation Introduction Vector spaces, linear independence, subspace The goal of Numerical Analysis is to compute approximations We want to approximate eg numbers in R or C vectors in R
More informationMathematical Analysis Outline. William G. Faris
Mathematical Analysis Outline William G. Faris January 8, 2007 2 Chapter 1 Metric spaces and continuous maps 1.1 Metric spaces A metric space is a set X together with a real distance function (x, x ) d(x,
More information2 Two-Point Boundary Value Problems
2 Two-Point Boundary Value Problems Another fundamental equation, in addition to the heat eq. and the wave eq., is Poisson s equation: n j=1 2 u x 2 j The unknown is the function u = u(x 1, x 2,..., x
More informationMAT 419 Lecture Notes Transcribed by Eowyn Cenek 6/1/2012
(Homework 1: Chapter 1: Exercises 1-7, 9, 11, 19, due Monday June 11th See also the course website for lectures, assignments, etc) Note: today s lecture is primarily about definitions Lots of definitions
More informationCHAPTER II HILBERT SPACES
CHAPTER II HILBERT SPACES 2.1 Geometry of Hilbert Spaces Definition 2.1.1. Let X be a complex linear space. An inner product on X is a function, : X X C which satisfies the following axioms : 1. y, x =
More informationBasic Elements of Linear Algebra
A Basic Review of Linear Algebra Nick West nickwest@stanfordedu September 16, 2010 Part I Basic Elements of Linear Algebra Although the subject of linear algebra is much broader than just vectors and matrices,
More informationFunctional Analysis. Martin Brokate. 1 Normed Spaces 2. 2 Hilbert Spaces The Principle of Uniform Boundedness 32
Functional Analysis Martin Brokate Contents 1 Normed Spaces 2 2 Hilbert Spaces 2 3 The Principle of Uniform Boundedness 32 4 Extension, Reflexivity, Separation 37 5 Compact subsets of C and L p 46 6 Weak
More informationBindel, Fall 2016 Matrix Computations (CS 6210) Notes for
1 Logistics Notes for 2016-08-29 General announcement: we are switching from weekly to bi-weekly homeworks (mostly because the course is much bigger than planned). If you want to do HW but are not formally
More informationGaussian Hilbert spaces
Gaussian Hilbert spaces Jordan Bell jordan.bell@gmail.com Department of Mathematics, University of Toronto July 11, 015 1 Gaussian measures Let γ be a Borel probability measure on. For a, if γ = δ a then
More informationv = v 1 2 +v 2 2. Two successive applications of this idea give the length of the vector v R 3 :
Length, Angle and the Inner Product The length (or norm) of a vector v R 2 (viewed as connecting the origin to a point (v 1,v 2 )) is easily determined by the Pythagorean Theorem and is denoted v : v =
More informationLecture 23: 6.1 Inner Products
Lecture 23: 6.1 Inner Products Wei-Ta Chu 2008/12/17 Definition An inner product on a real vector space V is a function that associates a real number u, vwith each pair of vectors u and v in V in such
More informationMAT 771 FUNCTIONAL ANALYSIS HOMEWORK 3. (1) Let V be the vector space of all bounded or unbounded sequences of complex numbers.
MAT 771 FUNCTIONAL ANALYSIS HOMEWORK 3 (1) Let V be the vector space of all bounded or unbounded sequences of complex numbers. (a) Define d : V V + {0} by d(x, y) = 1 ξ j η j 2 j 1 + ξ j η j. Show that
More informationChapter 1. Preliminaries. The purpose of this chapter is to provide some basic background information. Linear Space. Hilbert Space.
Chapter 1 Preliminaries The purpose of this chapter is to provide some basic background information. Linear Space Hilbert Space Basic Principles 1 2 Preliminaries Linear Space The notion of linear space
More informationMATH 426, TOPOLOGY. p 1.
MATH 426, TOPOLOGY THE p-norms In this document we assume an extended real line, where is an element greater than all real numbers; the interval notation [1, ] will be used to mean [1, ) { }. 1. THE p
More informationExercise Solutions to Functional Analysis
Exercise Solutions to Functional Analysis Note: References refer to M. Schechter, Principles of Functional Analysis Exersize that. Let φ,..., φ n be an orthonormal set in a Hilbert space H. Show n f n
More informationApplied Analysis (APPM 5440): Final exam 1:30pm 4:00pm, Dec. 14, Closed books.
Applied Analysis APPM 44: Final exam 1:3pm 4:pm, Dec. 14, 29. Closed books. Problem 1: 2p Set I = [, 1]. Prove that there is a continuous function u on I such that 1 ux 1 x sin ut 2 dt = cosx, x I. Define
More informationMATH 304 Linear Algebra Lecture 20: Review for Test 1.
MATH 304 Linear Algebra Lecture 20: Review for Test 1. Topics for Test 1 Part I: Elementary linear algebra (Leon 1.1 1.4, 2.1 2.2) Systems of linear equations: elementary operations, Gaussian elimination,
More informationLecture Notes DRE 7007 Mathematics, PhD
Eivind Eriksen Lecture Notes DRE 7007 Mathematics, PhD August 21, 2012 BI Norwegian Business School Contents 1 Basic Notions.................................................. 1 1.1 Sets......................................................
More informationReal Analysis Notes. Thomas Goller
Real Analysis Notes Thomas Goller September 4, 2011 Contents 1 Abstract Measure Spaces 2 1.1 Basic Definitions........................... 2 1.2 Measurable Functions........................ 2 1.3 Integration..............................
More informationMATH 304 Linear Algebra Lecture 19: Least squares problems (continued). Norms and inner products.
MATH 304 Linear Algebra Lecture 19: Least squares problems (continued). Norms and inner products. Orthogonal projection Theorem 1 Let V be a subspace of R n. Then any vector x R n is uniquely represented
More informationTest 1 Review Problems Spring 2015
Test Review Problems Spring 25 Let T HomV and let S be a subspace of V Define a map τ : V /S V /S by τv + S T v + S Is τ well-defined? If so when is it well-defined? If τ is well-defined is it a homomorphism?
More informationMATH 304 Linear Algebra Lecture 8: Vector spaces. Subspaces.
MATH 304 Linear Algebra Lecture 8: Vector spaces. Subspaces. Linear operations on vectors Let x = (x 1, x 2,...,x n ) and y = (y 1, y 2,...,y n ) be n-dimensional vectors, and r R be a scalar. Vector sum:
More informationAnalysis-3 lecture schemes
Analysis-3 lecture schemes (with Homeworks) 1 Csörgő István November, 2015 1 A jegyzet az ELTE Informatikai Kar 2015. évi Jegyzetpályázatának támogatásával készült Contents 1. Lesson 1 4 1.1. The Space
More informationVector Spaces. Vector space, ν, over the field of complex numbers, C, is a set of elements a, b,..., satisfying the following axioms.
Vector Spaces Vector space, ν, over the field of complex numbers, C, is a set of elements a, b,..., satisfying the following axioms. For each two vectors a, b ν there exists a summation procedure: a +
More informationJim Lambers MAT 610 Summer Session Lecture 2 Notes
Jim Lambers MAT 610 Summer Session 2009-10 Lecture 2 Notes These notes correspond to Sections 2.2-2.4 in the text. Vector Norms Given vectors x and y of length one, which are simply scalars x and y, the
More informationFunctional Analysis HW #5
Functional Analysis HW #5 Sangchul Lee October 29, 2015 Contents 1 Solutions........................................ 1 1 Solutions Exercise 3.4. Show that C([0, 1]) is not a Hilbert space, that is, there
More informationThe following definition is fundamental.
1. Some Basics from Linear Algebra With these notes, I will try and clarify certain topics that I only quickly mention in class. First and foremost, I will assume that you are familiar with many basic
More informationVector spaces. DS-GA 1013 / MATH-GA 2824 Optimization-based Data Analysis.
Vector spaces DS-GA 1013 / MATH-GA 2824 Optimization-based Data Analysis http://www.cims.nyu.edu/~cfgranda/pages/obda_fall17/index.html Carlos Fernandez-Granda Vector space Consists of: A set V A scalar
More informationMath 443 Differential Geometry Spring Handout 3: Bilinear and Quadratic Forms This handout should be read just before Chapter 4 of the textbook.
Math 443 Differential Geometry Spring 2013 Handout 3: Bilinear and Quadratic Forms This handout should be read just before Chapter 4 of the textbook. Endomorphisms of a Vector Space This handout discusses
More informationLecture Notes for Inf-Mat 3350/4350, Tom Lyche
Lecture Notes for Inf-Mat 3350/4350, 2007 Tom Lyche August 5, 2007 2 Contents Preface vii I A Review of Linear Algebra 1 1 Introduction 3 1.1 Notation............................... 3 2 Vectors 5 2.1 Vector
More informationAnalysis and Linear Algebra. Lectures 1-3 on the mathematical tools that will be used in C103
Analysis and Linear Algebra Lectures 1-3 on the mathematical tools that will be used in C103 Set Notation A, B sets AcB union A1B intersection A\B the set of objects in A that are not in B N. Empty set
More informationNumerical Linear Algebra
University of Alabama at Birmingham Department of Mathematics Numerical Linear Algebra Lecture Notes for MA 660 (1997 2014) Dr Nikolai Chernov April 2014 Chapter 0 Review of Linear Algebra 0.1 Matrices
More informationVector Spaces and Linear Transformations
Vector Spaces and Linear Transformations Wei Shi, Jinan University 2017.11.1 1 / 18 Definition (Field) A field F = {F, +, } is an algebraic structure formed by a set F, and closed under binary operations
More informationFUNCTIONAL ANALYSIS LECTURE NOTES: ADJOINTS IN HILBERT SPACES
FUNCTIONAL ANALYSIS LECTURE NOTES: ADJOINTS IN HILBERT SPACES CHRISTOPHER HEIL 1. Adjoints in Hilbert Spaces Recall that the dot product on R n is given by x y = x T y, while the dot product on C n is
More information1 Basics of vector space
Linear Algebra- Review And Beyond Lecture 1 In this lecture, we will talk about the most basic and important concept of linear algebra vector space. After the basics of vector space, I will introduce dual
More information(, ) : R n R n R. 1. It is bilinear, meaning it s linear in each argument: that is
17 Inner products Up until now, we have only examined the properties of vectors and matrices in R n. But normally, when we think of R n, we re really thinking of n-dimensional Euclidean space - that is,
More informationChapter 2. Vectors and Vector Spaces
2.1. Operations on Vectors 1 Chapter 2. Vectors and Vector Spaces Section 2.1. Operations on Vectors Note. In this section, we define several arithmetic operations on vectors (especially, vector addition
More informationDistances and similarities Based in part on slides from textbook, slides of Susan Holmes. October 3, Statistics 202: Data Mining
Distances and similarities Based in part on slides from textbook, slides of Susan Holmes October 3, 2012 1 / 1 Similarities Start with X which we assume is centered and standardized. The PCA loadings were
More informationProblem Set # 1 Solution, 18.06
Problem Set # 1 Solution, 1.06 For grading: Each problem worths 10 points, and there is points of extra credit in problem. The total maximum is 100. 1. (10pts) In Lecture 1, Prof. Strang drew the cone
More informationDefinitions for Quizzes
Definitions for Quizzes Italicized text (or something close to it) will be given to you. Plain text is (an example of) what you should write as a definition. [Bracketed text will not be given, nor does
More informationMATH 304 Linear Algebra Lecture 20: The Gram-Schmidt process (continued). Eigenvalues and eigenvectors.
MATH 304 Linear Algebra Lecture 20: The Gram-Schmidt process (continued). Eigenvalues and eigenvectors. Orthogonal sets Let V be a vector space with an inner product. Definition. Nonzero vectors v 1,v
More informationThe spectrum of a self-adjoint operator is a compact subset of R
The spectrum of a self-adjoint operator is a compact subset of R Jordan Bell jordan.bell@gmail.com Department of Mathematics, University of Toronto April 3, 2014 Abstract In these notes I prove that the
More informationMath 4377/6308 Advanced Linear Algebra
1.3 Subspaces Math 4377/6308 Advanced Linear Algebra 1.3 Subspaces Jiwen He Department of Mathematics, University of Houston jiwenhe@math.uh.edu math.uh.edu/ jiwenhe/math4377 Jiwen He, University of Houston
More informationApplied Linear Algebra in Geoscience Using MATLAB
Applied Linear Algebra in Geoscience Using MATLAB Contents Getting Started Creating Arrays Mathematical Operations with Arrays Using Script Files and Managing Data Two-Dimensional Plots Programming in
More information