4. General Properties of Irreducible Vectors and Operators 4.1 Irreducible Basis Vectors 4.2 The Reduction of Vectors — Projection Operators for Irreducible.

Slides:



Advertisements
Similar presentations
7. Rotations in 3-D Space – The Group SO(3)
Advertisements

8.4. Unitary Operators. Inner product preserving V, W inner product spaces over F in R or C. T:V -> W. T preserves inner products if (Ta|Tb) = (a|b) for.
Symmetric Matrices and Quadratic Forms
Chapter 5 Orthogonality
I. Homomorphisms & Isomorphisms II. Computing Linear Maps III. Matrix Operations VI. Change of Basis V. Projection Topics: Line of Best Fit Geometry of.
6. One-Dimensional Continuous Groups 6.1 The Rotation Group SO(2) 6.2 The Generator of SO(2) 6.3 Irreducible Representations of SO(2) 6.4 Invariant Integration.
3.III.1. Representing Linear Maps with Matrices 3.III.2. Any Matrix Represents a Linear Map 3.III. Computing Linear Maps.
8. The Group SU(2) and more about SO(3) SU(2) = Group of 2  2 unitary matrices with unit determinant. Simplest non-Abelian Lie group. Locally equivalent.
3.VI.1. Orthogonal Projection Into a Line 3.VI.2. Gram-Schmidt Orthogonalization 3.VI.3. Projection Into a Subspace 3.VI. Projection 3.VI.1. & 2. deal.
Probability theory 2011 The multivariate normal distribution  Characterizing properties of the univariate normal distribution  Different definitions.
8. The Group SU(2) and more about SO(3) SU(2) = Group of 2  2 unitary matrices with unit determinant. Simplest non-Abelian Lie group. Locally equivalent.
3-D Geometry.
3.V.1. Changing Representations of Vectors 3.V.2. Changing Map Representations 3.V. Change of Basis.
The Terms that You Have to Know! Basis, Linear independent, Orthogonal Column space, Row space, Rank Linear combination Linear transformation Inner product.
I. Isomorphisms II. Homomorphisms III. Computing Linear Maps IV. Matrix Operations V. Change of Basis VI. Projection Topics: Line of Best Fit Geometry.
Orthogonality and Least Squares
5 5.1 © 2012 Pearson Education, Inc. Eigenvalues and Eigenvectors EIGENVECTORS AND EIGENVALUES.
Orthogonal Sets (12/2/05) Recall that “orthogonal” matches the geometric idea of “perpendicular”. Definition. A set of vectors u 1,u 2,…,u p in R n is.
Dirac Notation and Spectral decomposition
Subspaces, Basis, Dimension, Rank
App III. Group Algebra & Reduction of Regular Representations 1. Group Algebra 2. Left Ideals, Projection Operators 3. Idempotents 4. Complete Reduction.
Matrices CS485/685 Computer Vision Dr. George Bebis.
5.1 Orthogonality.
Linear Algebra Review By Tim K. Marks UCSD Borrows heavily from: Jana Kosecka Virginia de Sa (UCSD) Cogsci 108F Linear.
Introduction The central problems of Linear Algebra are to study the properties of matrices and to investigate the solutions of systems of linear equations.
Section 6.6 Orthogonal Matrices.
राघव वर्मा Inner Product Spaces Physical properties of vectors  aka length and angles in case of arrows Lets use the dot product Length of Cosine of the.
GROUPS & THEIR REPRESENTATIONS: a card shuffling approach Wayne Lawton Department of Mathematics National University of Singapore S ,
Linear Algebra Review 1 CS479/679 Pattern Recognition Dr. George Bebis.
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.
8.1 Vector spaces A set of vector is said to form a linear vector space V Chapter 8 Matrices and vector spaces.
Chap 3. Formalism Hilbert Space Observables
Prolog Text Books: –W.K.Tung, "Group Theory in Physics", World Scientific (85) –J.F.Cornwell, "Group Theory in Physics", Vol.I, AP (85) Website:
AN ORTHOGONAL PROJECTION
Chap. 6 Linear Transformations
17. Group Theory 1.Introduction to Group Theory 2.Representation of Groups 3.Symmetry & Physics 4.Discrete Groups 5.Direct Products 6.Symmetric Groups.
4.8 Rank Rank enables one to relate matrices to vectors, and vice versa. Definition Let A be an m  n matrix. The rows of A may be viewed as row vectors.
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.
Quantum Two 1. 2 Angular Momentum and Rotations 3.
Quantum Two 1. 2 Angular Momentum and Rotations 3.
Quantum Two 1. 2 Angular Momentum and Rotations 3.
5.1 Eigenvectors and Eigenvalues 5. Eigenvalues and Eigenvectors.
3.Spherical Tensors Spherical tensors : Objects that transform like 2 nd tensors under rotations.  {Y l m ; m =  l, …, l } is a (2l+1)-D basis for (irreducible)
4.8 Rank Rank enables one to relate matrices to vectors, and vice versa. Definition Let A be an m  n matrix. The rows of A may be viewed as row vectors.
Instructor: Mircea Nicolescu Lecture 8 CS 485 / 685 Computer Vision.
Quantum Two 1. 2 Angular Momentum and Rotations 3.
Quantum Two.
Introduction The central problems of Linear Algebra are to study the properties of matrices and to investigate the solutions of systems of linear equations.
Introduction The central problems of Linear Algebra are to study the properties of matrices and to investigate the solutions of systems of linear equations.
Group.
Week 5 The Fourier series and transformation
Matrices and vector spaces
5. Direct Products The basis  of a system may be the direct product of other basis { j } if The system consists of more than one particle. More than.
Quantum Two.
GROUPS & THEIR REPRESENTATIONS: a card shuffling approach
Multivariate Analysis: Theory and Geometric Interpretation
Orthogonality and Least Squares
Chapter 3 Linear Algebra
Symmetric Matrices and Quadratic Forms
Quantum Two.
Linear Algebra Lecture 41.
3. Group Representations
Linear Vector Space and Matrix Mechanics
3.IV. Change of Basis 3.IV.1. Changing Representations of Vectors
Eigenvalues and Eigenvectors
3. Group Representations
NULL SPACES, COLUMN SPACES, AND LINEAR TRANSFORMATIONS
Orthogonality and Least Squares
Symmetric Matrices and Quadratic Forms
4. General Properties of Irreducible Vectors and Operators
Presentation transcript:

4. General Properties of Irreducible Vectors and Operators 4.1 Irreducible Basis Vectors 4.2 The Reduction of Vectors — Projection Operators for Irreducible Components 4.3 Irreducible Operators and the Wigner-Eckart Theorem Comments: States of system can be classified in terms of IRs Spherical symmetry  Y lm T d  Bloch functions Operators of system can be classified in terms of IRs x, p transforms under rotations as vectors T , F  as 2 nd rank tensors under Lorentz transformations

4.1. Irreducible Basis Vectors Notations: U(G) is an unitary rep of G on an inner product space V. V  is an invariant subspace of V wrt U(G). { e j  | j = 1, …, n  } is an orthonormal basis of V .  g  G where D  is the matrix IR wrt { e j  } Definition 4.1: Irreducible Basis Vectors ( IBV ) { e j  } is an irreducible set transforming according to the  –rep of G. e j  is said to belong to the j th row of the  –rep.

Generalization of the Orthogonality Theorem: Let IR  and be equivalent, i.e.,  S  D = S D  S  1. Thus, the Orthogonality Theorem can be generalized to where

Theorem 4.1: Let { u j  | j = 1, …, n  } & { v k | k = 1, …, n } be 2 IBVs wrt G on V. If  & are inequivalent, then { u j  } & { v k } are mutually orthogonal. Proof:  g  G QED

Let → Comparing with gives i.e.,  

Example: H-atom, G = R(3)

4.2. The Reduction of Vectors – Projection Operators for Irreducible Components Theorem 4.2: Let Then for any | x   V, is a set of IBVs that transform according to  , if not null, Proof: QED (  ) exempts  from sum rule

Theorem 4.3: Letbe a set of IBVs & Then Proof: QED If D  is orthogonal, then

Corollary 1: Proof: is a set of (un-normalized) IBVs Alternative proof:

Corollary 2: Proof: { e j  } is complete:  QED The matrix elements of U(g) wrt the IBVs are Hence ( Block diagonal  g )

Corollary 3: Proof: Cor. 2: ( Cor. 1 ) QED C.f. Proof of Theorem 4.2

Corollary 3: Alternative proof: QED

Definition 4.2: Projection Operators = Projection operator onto basis vector e  j = Projection operator onto irred invariant subspace V   P  j & P  are indeed projections Or:

Theorem 4.4: Completeness P  j & P  are complete, i.e., Proof:Letbe the basis of any irreducible invariant subspace V of V Thm 4.3: , k QED Alternatively: ( Provided { e  j } is complete, i.e., V is decomposible. )

Comments: Let U(G) be a rep of G on V. If U(G) is decomposable, then The corresponding complete set of IBVs is Then is not a projection, but it's useful in constructing IBVs

Example 1: Let V be the space of square integrable functions f(x) of 1 variable. Let G = { e, I S }, where I S x = –x. G  C 2 eISIS  1 11  2 1–1 For 1–D reps:

Problem 3.9 Due date:Monday, Apr 10. Mid-Term Take-Home Exam

Example 2:T d = { T(n) | n  Z } G = T d. V = Space of state vectors for a particle on a 1–D lattice. IR : Let | y  be any localized states in the unit cell b = lattice constant  | k, y  is an eigenstate of T(m) with eigenvalue e – i k m b (c.f. Chap 1) ( Prob 4.1 ) ( State periodic )  All distinct IBVs can be generated from | y  in the unit cell

Example 3: NH 3

1. Transform a basis to IBVs. E.g., From localized basis to IBVs( normal modes ) Time dependence of normal modes are harmonic Applications 2. Reduce direct product reps to IRs & evaluate C-GCs Prob 4.2

4.3. Irreducible Operators and the Wigner-Eckart Theorem Definition 4.3: Irreducible Operators ( tensors ) Operators { O  j | j = 1, …, n  } are irreducible corresponding to the IR  if Comments: Let { O  j } & { e j } be irreducible. Then i.e., O  j e k transforms according to D   implicit sum

Theorem 4.5 Wigner-Eckart Let { O  j } & { e  j } be irreducible. Then where = reduced matrix element Proof:Thm 4.1: QED sum over  See Tinkham for a more detailed proof.

Example: EM Transitions in Atoms,G = R(3) Photon ( s, ): s = 1, = –1, 0, +1 Atom: | j m  : m = –j, –j+1, …, j–1, j Transition rate W  | f | 2 O s = dipole operator Wigner-Eckart (  = 1) :

Transition w/o symmetry considerations j = j' = 1 Allowed transitions with branching ratios ( Inversion not considered )