Linear Vector Space and Matrix Mechanics

Slides:



Advertisements
Similar presentations
Matrix Representation
Advertisements

Quantum One: Lecture 17.
Quantum One: Lecture 16.
3.1 – Simplifying Algebraic Expressions
Ch 7.3: Systems of Linear Equations, Linear Independence, Eigenvalues
Chapter 2 Matrices Definition of a matrix.
Chapter 2 Systems of Linear Equations and Matrices Section 2.4 Multiplication of Matrices.
1.2 - Products Commutative Properties Addition: Multiplication:
3.5 Solution by Determinants. The Determinant of a Matrix The determinant of a matrix A is denoted by |A|. Determinants exist only for square matrices.
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.
ECON 1150 Matrix Operations Special Matrices
8.1 Vector spaces A set of vector is said to form a linear vector space V Chapter 8 Matrices and vector spaces.
Equation --- An equation is a mathematical statement that asserts the equality of twomathematicalstatement expressions. An equation involves an unknown,
Chapter 2 Systems of Linear Equations and Matrices
1 Ch. 4 Linear Models & Matrix Algebra Matrix algebra can be used: a. To express the system of equations in a compact manner. b. To find out whether solution.
Quantum One: Lecture Representation Independent Properties of Linear Operators 3.
1 Copyright © 2015, 2011, 2007 Pearson Education, Inc. Chapter 9-1 Exponential and Logarithmic Functions Chapter 9.
MAT 171 Precalculus Algebra T rigsted - Pilot Test Dr. Claude Moore - Cape Fear Community College CHAPTER 5: Exponential and Logarithmic Functions and.
Chapter 3 Determinants Linear Algebra. Ch03_2 3.1 Introduction to Determinants Definition The determinant of a 2  2 matrix A is denoted |A| and is given.
Chapter 2 Properties of Real Numbers VOCABULARY. Absolute Value  The distance from zero on the number line and the point representing a real number on.
ALGEBRA READINESS Chapter 5 Section 6.
Unit 5: Properties of Logarithms MEMORIZE THEM!!! Exponential Reasoning [1] [2] [3] [4] Cannot take logs of negative number [3b]
Equations, Properties and Inequalities Review Unit 6 6 th Grade Math.
The Mathematics for Chemists (I) (Fall Term, 2004) (Fall Term, 2005) (Fall Term, 2006) Department of Chemistry National Sun Yat-sen University 化學數學(一)
Solving Linear Equations and Inequalities Chapter 2.
LEARNING OUTCOMES At the end of this topic, student should be able to :  D efination of matrix  Identify the different types of matrices such as rectangular,
Chapter 61 Chapter 7 Review of Matrix Methods Including: Eigen Vectors, Eigen Values, Principle Components, Singular Value Decomposition.
Chapter 2 Copyright © 2015, 2011, 2007 Pearson Education, Inc. Chapter 2-1 Solving Linear Equations and Inequalities.
Properties of Algebra. 7 + ( ) = ( ) + 9.
Number Properties. Commutative Property of Addition Words: In a sum, you can add terms in any order. Numbers: 5 + (-6) Algebra: a + b b + a.
College Algebra Chapter 6 Matrices and Determinants and Applications
MTH108 Business Math I Lecture 20.
Mathematical Formulation of the Superposition Principle
Rules Of Differentiation And Their Use In Comparative Statics
College Algebra Chapter 4 Exponential and Logarithmic Functions
Linear Algebra Lecture 2.
LINEAR ALGEBRA.
Matrices and vector spaces
Chapter 2 Vocabulary Equations.
Solving Linear Equations and Inequalities
Chapter 2 Equations and Inequalities in One Variable
Algebra Vocabulary SOL 6.23.
ECON 213 Elements of Mathematics for Economists
Systems of First Order Linear Equations
Quantum One.
Chapter 2.4/2.6 Notes: Multiplying and Dividing Real Numbers
Quantum One.
College Algebra Chapter 4 Exponential and Logarithmic Functions
Algebra Vocabulary.
Use Inverse Matrices to Solve 2 Variable Linear Systems
Chapter 3 Linear Algebra
Linear Algebra Lecture 3.
Linear Vector Space and Matrix Mechanics
Copyright © 2014, 2010, 2007 Pearson Education, Inc.
Stationary State Approximate Methods
Linear Vector Space and Matrix Mechanics
Linear Vector Space and Matrix Mechanics
Linear Vector Space and Matrix Mechanics
Linear Vector Space and Matrix Mechanics
2 Chapter Chapter 2 Equations, Inequalities and Problem Solving.
Copyright © 2014, 2010, 2007 Pearson Education, Inc.
Algebra Vocabulary SOL 6.23.
Linear Vector Space and Matrix Mechanics
Reading: Chapter 1 in Shankar
Linear Vector Space and Matrix Mechanics
Stationary State Approximate Methods
Linear Vector Space and Matrix Mechanics
Linear Vector Space and Matrix Mechanics
Linear Vector Space and Matrix Mechanics
Linear Vector Space and Matrix Mechanics
Presentation transcript:

Linear Vector Space and Matrix Mechanics Chapter 1 Lecture 1.6 Dr. Arvind Kumar Physics Department NIT Jalandhar e.mail: iitd.arvind@gmail.com https://sites.google.com/site/karvindk2013/

Projection operator: idempotent

Example: What are conditions when is projection Operator? Ans: Given operator is Hermitian Also the square of the operator is If is normalized, then

Commutator algebra:

If two operators are Hermitian and their product is also hermitian then these operators commute. We write, Also, From above two equations, we get Which means two operators commute. x and px are dynamical variable but the product xpx is not because this product does not commute.

Functions of operator:

Hermitian adjoint of function operators: The adjoint of is given by

If operator is hermitian, then a function of operator which can be expanded as will be hermitian only if coefficients an are real numbers. In general is not hermitian even if

One more important relation involving function operator: Consider function operator eA in terms of power series: -----(1) Consider function, -----------(2) Where, λ is real number. Expanding f(λ) using taylor series, ----------(3)

Note that the values of derivatives can be written as, using (2), -----(4) Using (2), (3) and (4) and using f(0) = B, we get ---------(5)

The rule is not valid for operators. We use Campbell Baker Hausdorff formula, according to Which, ---------(6) F(A,B) is expressed as a infinite sum of multiple commutators of A and B. If A and B are two operators such that both commute with their commutator [A,B] i.e. If [A,[A,B]] = [B,[A,B]] = 0, then -------(7)

Inverse an operator: The inverse of an operator (if it exist) is defined by relation, Where, is the unit operator. Quotient of two operators:

Properties:

Unitary operators: A linear operator is said to be unitary if its inverse is equal to its adjoint Product of two unitary operators is also unitary Product of any number of unitary operators is also unitary