Can the Numerical Range of a Nilpotent Operator be a Disc? Akio Arimoto Department of Mathematics Musashi Institute of Technology.

Slides:



Advertisements
Similar presentations
Vector Spaces A set V is called a vector space over a set K denoted V(K) if is an Abelian group, is a field, and For every element vV and K there exists.
Advertisements

Vectors 1: An Introduction to Vectors Department of Mathematics University of Leicester.
10.4 Complex Vector Spaces.
5.4 Basis And Dimension.
8.3 Inverse Linear Transformations
8.2 Kernel And Range.
Section 18.4 Path-Dependent Vector Fields and Green’s Theorem.
1 D. R. Wilton ECE Dept. ECE 6382 Introduction to Linear Vector Spaces Reference: D.G. Dudley, “Mathematical Foundations for Electromagnetic Theory,” IEEE.
Chaper 3 Weak Topologies. Reflexive Space.Separabe Space. Uniform Convex Spaces.
Signal , Weight Vector Spaces and Linear Transformations
Signal , Weight Vector Spaces and Linear Transformations
Eigenvalues and Eigenvectors
Exercise 1- 1’ Prove that if a point B belongs to the affine / convex hull Aff/Conv (A 1, A 2, …, A k ) of points A 1, A 2,…, A k, then: Aff/Conv (A 1,
2.III. Basis and Dimension 1.Basis 2.Dimension 3.Vector Spaces and Linear Systems 4.Combining Subspaces.
Dimension of a Vector Space (11/9/05) Theorem. If the vector space V has a basis consisting of n vectors, then any set of more than n vectors in V must.
Tutorial 10 Iterative Methods and Matrix Norms. 2 In an iterative process, the k+1 step is defined via: Iterative processes Eigenvector decomposition.
5 5.1 © 2012 Pearson Education, Inc. Eigenvalues and Eigenvectors EIGENVECTORS AND EIGENVALUES.
Lecture 2: Geometry vs Linear Algebra Points-Vectors and Distance-Norm Shang-Hua Teng.
ENGG2013 Unit 5 Linear Combination & Linear Independence Jan, 2011.
Mathematics1 Mathematics 1 Applied Informatics Štefan BEREŽNÝ.
T he Separability Problem and its Variants in Quantum Entanglement Theory Nathaniel Johnston Institute for Quantum Computing University of Waterloo.
Linear Algebra Review By Tim K. Marks UCSD Borrows heavily from: Jana Kosecka Virginia de Sa (UCSD) Cogsci 108F Linear.
Scientific Computing Matrix Norms, Convergence, and Matrix Condition Numbers.
Orthogonal Matrices and Spectral Representation In Section 4.3 we saw that n  n matrix A was similar to a diagonal matrix if and only if it had n linearly.
1 MAC 2103 Module 10 lnner Product Spaces I. 2 Rev.F09 Learning Objectives Upon completing this module, you should be able to: 1. Define and find the.
Linear Least Squares Approximation. 2 Definition (point set case) Given a point set x 1, x 2, …, x n  R d, linear least squares fitting amounts to find.
1 February 24 Matrices 3.2 Matrices; Row reduction Standard form of a set of linear equations: Chapter 3 Linear Algebra Matrix of coefficients: Augmented.
Fundamentals from Linear Algebra Ghan S. Bhatt and Ali Sekmen Mathematical Sciences and Computer Science College of Engineering Tennessee State University.
Review of basic concepts and facts in linear algebra Matrix HITSZ Instructor: Zijun Luo Fall 2012.
CHAPTER FIVE Orthogonality Why orthogonal? Least square problem Accuracy of Numerical computation.
Linear Algebra Chapter 4 Vector Spaces.
4 4.2 © 2012 Pearson Education, Inc. Vector Spaces NULL SPACES, COLUMN SPACES, AND LINEAR TRANSFORMATIONS.
Chapter 2: Vector spaces
Section 4.1 Vectors in ℝ n. ℝ n Vectors Vector addition Scalar multiplication.
KINEMATICS of a ROLLING BALL Wayne Lawton Department of Mathematics National University of Singapore Lecture based on my student’s MSc.
MA4266 Topology Wayne Lawton Department of Mathematics S ,
DE Weak Form Linear System
MA5241 Lecture 1 TO BE COMPLETED
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.
March 24, 2006 Fixed Point Theory in Fréchet Space D. P. Dwiggins Systems Support Office of Admissions Department of Mathematical Sciences Analysis Seminar.
Probability Spaces A probability space is a triple (closed under Sample Space (any nonempty set), Set of Events a sigma-algebra over complementation and.
Signal & Weight Vector Spaces
Linear Programming Back to Cone  Motivation: From the proof of Affine Minkowski, we can see that if we know generators of a polyhedral cone, they.
Solving Scalar Linear Systems A Little Theory For Jacobi Iteration
MAT 4725 Numerical Analysis Section 7.1 Part I Norms of Vectors and Matrices
4.5: The Dimension of a Vector Space. Theorem 9 If a vector space V has a basis, then any set in V containing more than n vectors must be linearly dependent.
Summary of the Last Lecture This is our second lecture. In our first lecture, we discussed The vector spaces briefly and proved some basic inequalities.
4 Vector Spaces 4.1 Vector Spaces and Subspaces 4.2 Null Spaces, Column Spaces, and Linear Transformations 4.3 Linearly Independent Sets; Bases 4.4 Coordinate.
Eigenvalues, Zeros and Poles
1 Chapter 4 Geometry of Linear Programming  There are strong relationships between the geometrical and algebraic features of LP problems  Convenient.
Computation of the solutions of nonlinear polynomial systems
Eigenvalues and Eigenvectors
Basis and Dimension Basis Dimension Vector Spaces and Linear Systems
Lecture on Linear Algebra
Numerical Ranges in Modern Times 14th WONRA at Man-Duen Choi
Subspaces and Spanning Sets
2.III. Basis and Dimension
Stability Analysis of Linear Systems
Affine Spaces Def: Suppose
Linear Algebra Lecture 32.
Maths for Signals and Systems Linear Algebra in Engineering Lectures 4-5, Tuesday 18th October 2016 DR TANIA STATHAKI READER (ASSOCIATE PROFFESOR) IN.
Linear Algebra Lecture 20.
Back to Cone Motivation: From the proof of Affine Minkowski, we can see that if we know generators of a polyhedral cone, they can be used to describe.
Vector Spaces, Subspaces
Linear Vector Space and Matrix Mechanics
Null Spaces, Column Spaces, and Linear Transformations
NULL SPACES, COLUMN SPACES, AND LINEAR TRANSFORMATIONS
Eigenvalues and Eigenvectors
Chapter 2. Simplex method
Approximation of Functions
Presentation transcript:

Can the Numerical Range of a Nilpotent Operator be a Disc? Akio Arimoto Department of Mathematics Musashi Institute of Technology

: nilpotent linear operator with norm 1, i.e. Assumption 1: is a disc with the radius and the center at origin. Assumption 2: Conclusion : for some Assertion in this talk

Notation : Numerical Radius : Numerical range : a unit ball in a Hilbert space

Known results For a 2x2 matrix with eigenvalues is an elliptical disc with as foci minor axis major axis Chi-Kwong Li, Proceeding of AMS, 1996, vol 124, no.7,

Toeplitz- Hausdorff ‘s Theorem is a convex set in the Gauss plane. O.Toeplitz, Das algebraische Analogon zu einem Satz von Fejer, Math.Z.2(1918), F.Hausdorff, Der Wertvorat einer Bilinearform, Math.Z.(1919),

Some Examples Ex.1.

Ex.2.

Ex.3.

Ex.4.

Ex.5.

Ex.6. My undergraduate student Aono found the following example. Counter example for Karaev’s paper(2004,Proceedings of AMS)

Ex.6. shows that nilpotency is not a sufficient condition for to be a disc. Indeed This is my motivation to start this study.

Haargerup and de la Harpe [HH] shown that for a nilpotent This is a consequence of a Fejer theorem :

Suppose and that there exists a unit vector with Let be the linear span of Theorem A.[HH p.375] satisfies

Thenis an n- dimensional subspace of and the restriction ofto is unitarily equivalent to the n-dimensional shift on We can restrict our problem to a finite matrix case even for the infinite dimensional space!

Lemma is a disc with the radius and the center at zero. See example 2

Proof of the Lemma

is unitary.

for where must be a discso because of Hausdorff-Toeplitz theorem.

If we take a unit vector The Haagerup - de la Harpe’s inequality must be the equality Q.E.D. we have

Theorem B.[HH,p.374] Let Ifthen

Theorem 1. (by Arimoto) is a disc.

Proof of Theorem For some ( from Theorem B)

are linearly independent, so

we now define by using the same

where we used

for any θ Apply again the Toeplitz-Hausdorff theorem, is a disc with the radius

References [HH] Uffe Haagerup and Pierre de la Harpe, The Numerical Radius of a Nilpotent Operator on a Hilbert Space, Proceedings of Amer.Math.Soc. 115,(1992) [K] Mubariz T. Karaev, The Numerical Range of a Nilpotent Operator on a Hilbert Space, Proc. Amer.Math.Soc.,2004 [Wu]Pey-Yuan Wu( 呉培元) Polygons and Numerical ranges,Mathematical Monthly,107(2000)pp [Wu-Gau]P-Y.Wu and Hwa-Long Gau( 高) Numerical Range of S(Φ),Linear and Multilinear Algebra 45(1998),pp.49-73

Poncelet’s theorem Algebraic curves of order 2 (examples: ellipes)

Poncelet’s theorem If for some Then starting from any other on

nxn matrix then being unit circle center 0 and has Poncelet ‘s property

Starting from any point on We have an n+1-gon Also see Hwa-Long Gau and Pei Yuan Wu Numerical range and Poncelet property Taiwanese J.Math, vol.7,no (2003)