Angles between Subspaces of an n-inner Product Space

Similar documents
Section 1.1. Introduction to R n

Similarity and Diagonalization. Similar Matrices

Recall that two vectors in are perpendicular or orthogonal provided that their dot

MATH 304 Linear Algebra Lecture 20: Inner product spaces. Orthogonal sets.

Inner product. Definition of inner product

Orthogonal Projections and Orthonormal Bases

Section 4.4 Inner Product Spaces

Dot product and vector projections (Sect. 12.3) There are two main ways to introduce the dot product

Inner Product Spaces and Orthogonality

WHEN DOES A CROSS PRODUCT ON R n EXIST?

Orthogonal Diagonalization of Symmetric Matrices

α = u v. In other words, Orthogonal Projection

Linear Algebra Notes for Marsden and Tromba Vector Calculus

Linear Algebra Review. Vectors

Section Inner Products and Norms

MAT 1341: REVIEW II SANGHOON BAEK

Vectors Math 122 Calculus III D Joyce, Fall 2012

Math 215 HW #6 Solutions

Numerical Analysis Lecture Notes

28 CHAPTER 1. VECTORS AND THE GEOMETRY OF SPACE. v x. u y v z u z v y u y u z. v y v z

1.3. DOT PRODUCT If θ is the angle (between 0 and π) between two non-zero vectors u and v,

Recall the basic property of the transpose (for any A): v A t Aw = v w, v, w R n.

x1 x 2 x 3 y 1 y 2 y 3 x 1 y 2 x 2 y 1 0.

Lectures notes on orthogonal matrices (with exercises) Linear Algebra II - Spring 2004 by D. Klain

Geometry of Vectors. 1 Cartesian Coordinates. Carlo Tomasi

Chapter 6. Orthogonality

Applied Linear Algebra I Review page 1

17. Inner product spaces Definition Let V be a real vector space. An inner product on V is a function

Linear Algebra: Vectors

MATH 423 Linear Algebra II Lecture 38: Generalized eigenvectors. Jordan canonical form (continued).

Math 550 Notes. Chapter 7. Jesse Crawford. Department of Mathematics Tarleton State University. Fall 2010

Mechanics 1: Vectors

4: SINGLE-PERIOD MARKET MODELS

Inner Product Spaces

ISOMETRIES OF R n KEITH CONRAD

Metric Spaces. Chapter Metrics

MATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix.

3. INNER PRODUCT SPACES

BANACH AND HILBERT SPACE REVIEW

Cross product and determinants (Sect. 12.4) Two main ways to introduce the cross product

Mathematics (MAT) MAT 061 Basic Euclidean Geometry 3 Hours. MAT 051 Pre-Algebra 4 Hours

1 VECTOR SPACES AND SUBSPACES

Invariant Metrics with Nonnegative Curvature on Compact Lie Groups

13 MATH FACTS a = The elements of a vector have a graphical interpretation, which is particularly easy to see in two or three dimensions.

Mathematics Course 111: Algebra I Part IV: Vector Spaces

Essential Mathematics for Computer Graphics fast

1 Inner Products and Norms on Real Vector Spaces

160 CHAPTER 4. VECTOR SPACES

MATH1231 Algebra, 2015 Chapter 7: Linear maps

Tail inequalities for order statistics of log-concave vectors and applications

1 Vectors: Geometric Approach

Smarandache Curves in Minkowski Space-time

Duality of linear conic problems

Problem Set 5 Due: In class Thursday, Oct. 18 Late papers will be accepted until 1:00 PM Friday.

Vector Spaces; the Space R n

Figure 1.1 Vector A and Vector F

13.4 THE CROSS PRODUCT

by the matrix A results in a vector which is a reflection of the given

Au = = = 3u. Aw = = = 2w. so the action of A on u and w is very easy to picture: it simply amounts to a stretching by 3 and 2, respectively.

Bindel, Spring 2012 Intro to Scientific Computing (CS 3220) Week 3: Wednesday, Feb 8

11.1. Objectives. Component Form of a Vector. Component Form of a Vector. Component Form of a Vector. Vectors and the Geometry of Space

Notes on Orthogonal and Symmetric Matrices MENU, Winter 2013

On duality of modular G-Riesz bases and G-Riesz bases in Hilbert C*-modules

MAT 242 Test 3 SOLUTIONS, FORM A

THREE DIMENSIONAL GEOMETRY

Lecture L3 - Vectors, Matrices and Coordinate Transformations

NEW YORK STATE TEACHER CERTIFICATION EXAMINATIONS

Max-Min Representation of Piecewise Linear Functions

Chapter 20. Vector Spaces and Bases

INVARIANT METRICS WITH NONNEGATIVE CURVATURE ON COMPACT LIE GROUPS

On the representability of the bi-uniform matroid

9 Multiplication of Vectors: The Scalar or Dot Product

v w is orthogonal to both v and w. the three vectors v, w and v w form a right-handed set of vectors.

Elementary Linear Algebra

v 1 v 3 u v = (( 1)4 (3)2, [1(4) ( 2)2], 1(3) ( 2)( 1)) = ( 10, 8, 1) (d) u (v w) = (u w)v (u v)w (Relationship between dot and cross product)

1. Let P be the space of all polynomials (of one real variable and with real coefficients) with the norm

Week 1 Chapter 1: Fundamentals of Geometry. Week 2 Chapter 1: Fundamentals of Geometry. Week 3 Chapter 1: Fundamentals of Geometry Chapter 1 Test

Chapter 17. Orthogonal Matrices and Symmetries of Space

Math 241, Exam 1 Information.

3 Orthogonal Vectors and Matrices

MATH APPLIED MATRIX THEORY

Geometry Course Summary Department: Math. Semester 1

LEARNING OBJECTIVES FOR THIS CHAPTER

NOTES ON LINEAR TRANSFORMATIONS

Unified Lecture # 4 Vectors

Irreducibility criteria for compositions and multiplicative convolutions of polynomials with integer coefficients

Computing Orthonormal Sets in 2D, 3D, and 4D

1 Sets and Set Notation.

State of Stress at Point

Vector and Matrix Norms

Lecture 14: Section 3.3

Chapter 6. Cuboids. and. vol(conv(p ))

Math 4310 Handout - Quotient Vector Spaces

Systems of Linear Equations

Introduction to General and Generalized Linear Models

Questions. Strategies August/September Number Theory. What is meant by a number being evenly divisible by another number?

Orthogonal Bases and the QR Algorithm

Solutions to Math 51 First Exam January 29, 2015

Mean value theorem, Taylors Theorem, Maxima and Minima.

Transcription:

Angles between Subspaces of an n-inner Product Space M. Nur 1, H. Gunawan 2, O. Neswan 3 1,2,3 Analysis Geometry group, Faculty of Mathematics Natural Sciences, Bung Institute of Technology, Jl. Ganesha 10, Bung 40312 email: 1 nur_math@student.itb.ac.id, 2 hgunawan@math.itb.ac.id, 3 okineswan@math.itb.ac.id. Abstract We discuss angles between two subspaces of an inner product space. This paper is an extension of the work by Gunawan et al [7]. We present an explicit formula for angles between two subspaces of an n-inner product space. Moreover, we study its connection with angles in an inner product space. Key Words: angles, inner product space, n-inner product space. 1. Introduction In an inner product space, we can calculate angles between two subspaces. Since the s, the concept of angles between two subspaces of the Euclidean space has been studied by many researchers [2]. Application of angles between two subspaces in an inner product space can be found in the fields of computing statistics. For example, measuring the similarity of images of three-dimensional objects is invariant under the displacement of the object the physic of the camera [8]. In statistics, the angle between two subspaces is related to canonical (or principal) angles which are measures of dependency of one set of rom variables on another [1]. In 2001, Risteksi Trencevski [11] introduced a definition of angles between two subspaces of using determinant Gram explained their connection with canonical angles. Gunawan et al. [6,7] refined their definition gave the formulas for angles between two subspaces in an inner product space of arbitrary dimension. They also explained the connection with canonical angles by using elementary calculus linear algebra some application examples. Let be a real inner product space. If is a -dimensional subspace is a -dimensional subspace of, then the angle between subspaces isdefined by with. In formula, denotes the (orthogonal) projection of on. Gunawan et al [8] showed that the value of is equal to the ratio between the length of the projection of on the length of ( ). Likewise, if are - dimensional subspaces of that intersects on -dimensional subspace with then the angle between is defined by with with are the orthogonal complement of, respectively, on. Gunawan et al. showed that the value of is equal to the ratio between the volume of the -dimensional parallelepiped spanned by the projection of on the volume of the -dimensional parallelepiped spanned by. Page 1

In this paper, we will give some explicit formulas for angles between two subspaces in various cases of an inner product space of arbitrary dimension. This research is a further development of the work of Gunawan et al. In the next section, we will formulate angles in an -inner product space will show the connection between angles in an inner product space in an -inner product space. 2. Main Results 2.1 Angles between subspaces in an inner product space In this subsection, we will discuss angles between subspaces in an inner product space. Let be an inner product space consider the star -inner product as in [4,10]. Then, the following function defines the stard -norm. Geometrically, if then is the length of. If then is the area of the parallelogram spanned by the vectors in. Thus, in general represents the volume of the - dimensional parallelepiped spanned by. As in [7], we define the angle between subspaces of an inner product space by using the stard -inner product, as follows. Definition 1. [7] Let be a real inner product space. If is a - dimensional subspace is a -dimensional subspace of with, then the angle between subspaces is defined by with, where denote the projection of on for each denotes the stard -norm on. According to Definition 1 for case that can be obtained as follows., we have an explisit formula the cosine of the angle Proposition 2. Let be a real inner product space. If are -dimensional subspaces of then the angle between subspaces is with ( [ ]). Proof. The projection of on for may be expressed as Observe that for. Hence we have Page 2

Next observe that Consequently, we have As a consequence of this formula, we have Kurepa s generalization of the Cauchy-Schwarz inequality (see [9]). Next, we will determine an formula for the cosine of the angle between subspaces that intersects on subspaces of. The formula for the angle can be obtained as follows. Proposition 3. Let be a real inner product space. If is a -dimensional subspace is a -dimensional subspace of that intersects on -dimensional subspace with then the angle between subspaces is with where are the orthogonal complement of, respectively, on. Proof. The projection of on is. Next, we may write where is the projection of on is the orthogonal complement of on. In line with this, we may write where is the projection of on is the orthogonal complement of on. Using the stard -norm, we obtain Page 3

This formula tells us that the value of is equal to the ratio between the length of the orthogonal complement of on the length of the projection of on. More generally, the angle that intersects on subspaces of is poured in following theorem: Theorem 4. Let be a real inner product space. If is a -dimensional subspace is a - dimensional subspace on with that intersects on -dimensional subspace with then the angle between subspaces is with where are the orthogonal complement of, respectively, on for. Proof. The projection of on is. Next, we may write where is the projection of on is the orthogonal complement of on. In line with this, we may write where is the projection of on is the orthogonal complement of on for. Using the stard -norm, we obtain 2.2. Angles between subspaces in an -inner product space In this subsection, we will discuss angles between subspaces in an -inner product space its connection with angles in an inner product space. Let be a real -inner product space with. Fix a linearly independent set in with respect to, define the function by for each. Then we have the following proposition. Proposition 5. [3] The function defines an inner product on. Corollary 6. Let function be a -norm that induced from an -inner product. The following [ ] defines a norm that corresponds to an inner product pada. Page 4

Using an inner product, we have a new stard -inner product on, namely a new stard -norm. Furthermore, one may also use an inner product its induced norm to study the angle between subspaces in an -inner product space. Inspired by Definition 1 with the new stard -norm, we define the angle between subspaces in an -inner product space. Definition 7. Let be a real -inner product space. If is a -dimensional subspace is a -dimensional subspace of with, then the angle between subspaces is defined by with ( ) ( ) where denote the projection of on for each denotes the stard -norm on. According to Definition 7, we will determine an formula for the cosine of the angle between subspaces that intersects on subspaces of. The formula for the angle can be obtained as follows. Proposition 8. Let be a real -inner product space. If is a -dimensional subspace is a -dimensional subspace on that intersects on - dimensional subspace with then the angle between subspaces is, with ( ) ( ) orthogonal complement of, respectively, on. where are the Proof. Writing using the new stard -norm, we obtain ( ) ( ) Using Definition 7 following the proof of Theorem 4, the angle with can be obtained as follows. Theorem 9. Let be a real -inner product space. If is a -dimensional subspace Page 5

is a -dimensional subspace on with that intersects on -dimensional subspace with then the angle between subspaces is, with ( ) ( ) where are the orthogonal complement of, respectively, on for. Before we discuss the connection between angles in an inner product space in an -inner product space, we have equivalent norm on with norm where are an orthonormal set on as follows. Proposition 10. [6] Norm equivalent with norm that corresponds to the inner product on. Namely, for every. From this proposition, we have the connection between angles in an inner product space in an -inner product space, namely Theorem 11. If is the angle between subspaces of is the angle between subspaces of with, for for then Proof. Writing,. Next, we observe that ( ) According to Proposition 3 8, we have ( ) ( ). Hence, we obtain By Theorem 11 for, the value of is equal to the value of. Nevertheless, the upper bound of for is inappropriate because its value is greater than 1. If then the lower bound of is. 3. References [1] T. W. Anderson, "An Introduction to Multivariate Statistical Analysis", John Wiley Sons, Inc. New York (1958). [2] C. Davis W. Kahan, "The rotation of eigenvectors by a perturbation", III. SIAM J. Numer. Anal. 7 (1970),1-46. [3] H. Gunawan, "Inner Products On -Inner Products Spaces", Soochow Journal of Mathemathics.28(4) (2002),389-398. Page 6

[4] H. Gunawan, "On -Inner products, -Norms, The Cauchy-Schwarz Inequality", Sci. Math. Jpn. 55 (2002), 53-60. [5] H. Gunawan M. Mashadi,"On -Normed Spaces", Int. J. Math. Sci. 27 (2001), 321-329. [6] H. Gunawan O. Neswan, "On Angles Between Subspaces of Inner Product Spaces", J. Indones. Math. Soc.11(2005). [7] H. Gunawan, O. Neswan W. Setya-Budhi, "A Formula for Angles between Two Subspaces of Inner Product Spaces", Beitr. zur Algebra und Geometrie Contributions to Algebra Geometry. 46(2) (2005), 311-320. [8] Y. Igarashi K. Fukui, "3D Object Recognition Based on Canonical Angles between Shape Subspaces", Springer ACCV 10 Volume Part IV. (2010), 580-591. [9] S. Kurepa, "On the Buniakowsky-Cauchy-Schwarz inequality", Glas. Mat. III. Ser.1(21) (1966), 147-158. [10] A. Misiak, " -Inner Product Spaces", Math. Nachr. 140 (1989),299-319. [11] I.B. Risteski K.G. Trencevski, "Principal Values Principal Subspaces of Two Subspaces of Vector Spaces with Inner Product", Beitr. Algebra Geom. 42 (2001), 289-300. Page 7