Section 6.1 Inner Products.

Slides:



Advertisements
Similar presentations
10.4 Complex Vector Spaces.
Advertisements

Chapter 4 Euclidean Vector Spaces
Chapter 6 Vocabulary.
Euclidean m-Space & Linear Equations Euclidean m-space.
6 6.1 © 2012 Pearson Education, Inc. Orthogonality and Least Squares INNER PRODUCT, LENGTH, AND ORTHOGONALITY.
10.6 Vectors in Space.
Basic Math Vectors and Scalars Addition/Subtraction of Vectors Unit Vectors Dot Product.
6 6.1 © 2012 Pearson Education, Inc. Orthogonality and Least Squares INNER PRODUCT, LENGTH, AND ORTHOGONALITY.
Chapter 12 – Vectors and the Geometry of Space
Lecture 2: Geometry vs Linear Algebra Points-Vectors and Distance-Norm Shang-Hua Teng.
Geometry of R 2 and R 3 Vectors in R 2 and R 3. NOTATION RThe set of real numbers R 2 The set of ordered pairs of real numbers R 3 The set of ordered.
C HAPTER 4 Inner Product & Orthogonality. C HAPTER O UTLINE Introduction Norm of the Vector, Examples of Inner Product Space - Euclidean n-space - Function.
Multiplication with Vectors
Length and Dot Product in R n Notes: is called a unit vector. Notes: The length of a vector is also called its norm. Chapter 5 Inner Product Spaces.
Elementary Linear Algebra Howard Anton Copyright © 2010 by John Wiley & Sons, Inc. All rights reserved. Chapter 3.
Chapter 5 Orthogonality.
University of Texas at Austin CS384G - Computer Graphics Fall 2008 Don Fussell Orthogonal Functions and Fourier Series.
Section 5.1 Real Vector Spaces. DEFINITION OF A VECTOR SPACE Let V be any non-empty set of objects on which two operations are defined: addition and multiplication.
Chapter 3 Euclidean Vector Spaces Vectors in n-space Norm, Dot Product, and Distance in n-space Orthogonality
Chapter 3 Vectors in n-space Norm, Dot Product, and Distance in n-space Orthogonality.
Section 9.1 Polar Coordinates. x OriginPole Polar axis.
Chapter 7 Inner Product Spaces 大葉大學 資訊工程系 黃鈴玲 Linear Algebra.
Copyright © Cengage Learning. All rights reserved. Vectors in Two and Three Dimensions.
Elementary Linear Algebra Anton & Rorres, 9th Edition
Main Menu Salas, Hille, Etgen Calculus: One and Several Variables Copyright 2003 © John Wiley & Sons, Inc. All rights reserved. Chapter 12: Vectors Cartesian.
Inner Products, Length, and Orthogonality (11/30/05) If v and w are vectors in R n, then their inner (or dot) product is: v  w = v T w That is, you multiply.
Lecture 11 Inner Product Space
VECTORS (Ch. 12) Vectors in the plane Definition: A vector v in the Cartesian plane is an ordered pair of real numbers:  a,b . We write v =  a,b  and.
Section 5.1 Length and Dot Product in ℝ n. Let v = ‹v 1­­, v 2, v 3,..., v n › and w = ‹w 1­­, w 2, w 3,..., w n › be vectors in ℝ n. The dot product.
Chap. 5 Inner Product Spaces 5.1 Length and Dot Product in R n 5.2 Inner Product Spaces 5.3 Orthonormal Bases: Gram-Schmidt Process 5.4 Mathematical Models.
Chapter 4 Euclidean n-Space Linear Transformations from to Properties of Linear Transformations to Linear Transformations and Polynomials.
Meeting 23 Vectors. Vectors in 2-Space, 3-Space, and n- Space We will denote vectors in boldface type such as a, b, v, w, and x, and we will denote scalars.
Inner Product, Length and Orthogonality Prepared by Vince Zaccone For Campus Learning Assistance Services at UCSB.
Section 11.2 Space Coordinates and Vectors in Space.
Section 6.2 Angles and Orthogonality in Inner Product Spaces.
Ch7_ Inner Product Spaces In this section, we extend those concepts of R n such as: dot product of two vectors, norm of a vector, angle between vectors,
Discrete Math Section 12.4 Define and apply the dot product of vectors Consider the vector equations; (x,y) = (1,4) + t its slope is 3/2 (x,y) = (-2,5)
Section 3.3 Dot Product; Projections. THE DOT PRODUCT If u and v are vectors in 2- or 3-space and θ is the angle between u and v, then the dot product.
Chapter 4 Vector Spaces Linear Algebra. Ch04_2 Definition 1: ……………………………………………………………………. The elements in R n called …………. 4.1 The vector Space R n Addition.
Section 4.1 Euclidean n-Space.
Vectors in the Plane 8.3 Part 1. 2  Write vectors as linear combinations of unit vectors.  Find the direction angles of vectors.  Use vectors to model.
An inner product on a vector space V is a function that, to each pair of vectors u and v in V, associates a real number and satisfies the following.
Chapter 4 Vector Spaces Linear Algebra. Ch04_2 Definition 1. Let be a sequence of n real numbers. The set of all such sequences is called n-space (or.
Lecture 11 Inner Product Spaces Last Time Change of Basis (Cont.) Length and Dot Product in R n Inner Product Spaces Elementary Linear Algebra R. Larsen.
Chapter 7 Inner Product Spaces
Dot Product of Vectors.
Spaces.
Matrix Arithmetic Prepared by Vince Zaccone
Lecture 7 Vector Space Last Time - Properties of Determinants
Section 3.4 Cross Product.
Elementary Linear Algebra
Lecture 10 Inner Product Spaces
Week 2 Introduction to Hilbert spaces Review of vector spaces
Inner Product, Length and Orthogonality
Section 4.1: Vector Spaces and Subspaces
Section 4.1: Vector Spaces and Subspaces
C H A P T E R 3 Vectors in 2-Space and 3-Space
10.2 Space Coordinates and Vectors in Space
Vectors, Linear Combinations and Linear Independence
Linear Algebra Chapter 4 Vector Spaces.
1.3 Vector Equations.
Homework Questions!.
Linear Algebra Lecture 38.
Lecture 2: Geometry vs Linear Algebra Points-Vectors and Distance-Norm
Elementary Linear Algebra Anton & Rorres, 9th Edition
Linear Algebra Lecture 20.
Linear Equations and Vectors
Honors Precalculus 4/19/18 IDs on and showing
RAYAT SHIKSHAN SANSTHA’S S.M.JOSHI COLLEGE HADAPSAR, PUNE
CHAPTER 3 VECTORS NHAA/IMK/UNIMAP.
Presentation transcript:

Section 6.1 Inner Products

INNER PRODUCT SPACES 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 that the following axioms are satisfied for all vectors u, v, and w in V and all scalars k. (a) ‹u, v› = ‹v, u› [Symmetry Axiom] (b) ‹u + v, z› = ‹u , z› + ‹v , z› [Additivity Axiom] (c) ‹ku , v› = k ‹u , v› [Homogeneity Axiom] (d) ‹v, v› ≥ 0 [Positivity Axiom] and ‹v, v› = 0 if and only if v = 0 A real vector space with an inner product is called a real inner product space.

WEIGHTED EUCLIDEAN INNER PRODUCT If w1, w2, . . . , wn are positive real numbers, called weights, and if u = (u1, u2, . . . , un) and v = (v1, v2, . . . , vn) are vectors in Rn, then it can be shown that the formula defines an inner product on Rn; it is called the weighted Euclidean inner product with weights w1, w2, . . . , wn.

NORM AND DISTANCE If V is an inner product space, then the norm (or length) of a vector u in V is denoted by ||u|| and is defined by ||u|| = ‹u, u›1/2 The distance between two vectors (points) u and v is denoted by d(u, v) and is defined by d(u, v) = ||u − v||

THE UNIT SPHERE The set of all points (vectors) in V that satisfy ||u|| = 1 is called the unit sphere ( or sometimes the unit circle) in V. In R2 and R3 these are points that lie 1 unit, in terms of the inner product, away from the origin. If you are using a different inner product than the dot product (e.g., the weighted Euclidean inner product), the “unit circle” may not be a circle!

PROPERTIES OF INNER PRODUCTS Theorem 6.1.1: If u, v, and w are vectors in a real inner product space, and k is any scalar, then