763622S ADVANCED QUANTUM MECHANICS Solution Set 1 Spring c n a n. c n 2 = 1.

Size: px
Start display at page:

Download "763622S ADVANCED QUANTUM MECHANICS Solution Set 1 Spring c n a n. c n 2 = 1."

Transcription

1 7636S ADVANCED QUANTUM MECHANICS Soluton Set 1 Sprng Warm-up Show that the egenvalues of a Hermtan operator  are real and that the egenkets correspondng to dfferent egenvalues are orthogonal (b) Show that f the state ket ψ = n c n a n s normalzed, then the expanson coeffcents c n must satsfy c n = 1 Soluton n A number c s shown to be real f c = c Let us study a Hermtan operator  for whch  =  holds, and  a = a a Based on the evaluaton of the nner product a  a = a a a = a On the other hand, we can use  =  and let that operate to the left: a  a = ( a  ) a = a a a = a a a So we have that a = a and so a s real Let now  b = b b, and examne the nner product a  b : Agan, lettng  act to the left, a  b = b a b b b a  b = a a b a a Takng the dfference of the two above equatons, one gets (b a) a b = 0 By assumpton a and b are dfferent egenvalues, and so we must have a b = 0

2 (b) Straght-forward calculaton ( ) ( ) α α = c a a c a a = c ac a a a = a a a,a a Normalzaton mples that α α = 1, and so c a = 1 a c a Change of bass Prove the theorem regardng the change of bass from the lecture notes: If both the bass { a n } and { b n } are orthonormal and complete, then there exsts a untary operator Û such that b 1 = Û a 1, b = Û a, b 3 = Û a 3, (1) Soluton The proof has three stages: Constructon of the operator Û, proof of property (1), and proof of untarty Frst part s rather easy, the operator should take a state a n and produce b n : ths would be accomplshed by b n a n To make Û work on any gven nput state a n, just take the sum: Û = n b n a n To see f ths gves property (1), let ths Û operate on an arbtrary a : Û a = n b n a n a = b Therefore, Û works as advertsed Next, test whether Û s untary: ÛÛ = j = j = 1, b j a j ( b a ) a b b j a j a δ j b = b b where the last equalty follows from the completeness of the bass { b } 3 Spn operators Consder the spn operators Ŝx, Ŝy, and Ŝz n the { S z ;, S z ; } bass Wrte out the operators Ŝx, Ŝy, and Ŝz n the { S z ;, S z ; } bass (b) Compute the commutators [Ŝx, Ŝy] and [Ŝ, Ŝx] as well as the ant-commutator {Ŝx, Ŝy} (c) Let us defne the ladder operators Ŝ± = Ŝx ± Ŝy Compute Ŝ± S z ; and Ŝ± S z ;

3 Soluton Let us frst summarze the { S z ;, zdn} bass represented wth the egenstates of Ŝx and Ŝy operators wth proper phase choce (see eg J J Sakura, Modern Quantum Mechancs, p 8): S z ; = 1 ( S x ; + S x ; ) S z ; = 1 ( S x ; S x ; ) (a) S z ; = 1 ( S y ; + S y ; ) S z ; = ( S y ; + S y ; ) (b) Representaton of an operator ˆB n the { S z ;, S z ; } bass: ˆB = S z ; j S z ; j ˆB S z ; k S z ; k = B jk S z ; j S z ; k j= k= j= k= and n matrx representaton we use the followng conventon wth the ndces: B = B jk = S z ; j ˆB S z ; k ( ) ( B B Sz ; = ˆB S z ; S z ; ˆB S z ; B B S z ; ˆB S z ; S z ; ˆB S z ; ) Bass representaton for operator Ŝx s after above defntons just the calculaton of the matrx elements B jk : ( ) Sz ; Ŝz S S z = z ; S z ; Ŝz S z ; S z ; Ŝz S z ; S z ; Ŝz S z ; = h ( ) Sz ; S z ; S z ; S z ; = h ( ) 1 0 S z ; S z ; S z ; S z ; 0 1 To do the same for Ŝx,y, we resort to relatons () and fnd out that Ŝ x S z ; = h S z; Ŝ y S z ; = h S z; Ŝ x S z ; = h S z; Ŝ y S z ; = h S z; whch shows that ( ) Sz ; Ŝx S S x = z ; S z ; Ŝx S z ; = h ( ) 0 1 S z ; Ŝx S z ; S z ; Ŝx S z ; 1 0 ( ) Sz ; Ŝy S S y = z ; S z ; Ŝy S z ; = h ( ) 0 S z ; Ŝy S z ; S z ; Ŝy S z ; 0 (b) As we now have the representatons of operators Ŝ n the Ŝz egenstate bass we can use them to calculate the (ant)commutators [( ) ( ) ( ) ( )] [S x, S y ] = S x S y S y S x = h = hs z (The general rule goes [S, S j ] = hɛ jk S k, where ɛ jk s the Lev-Cvta permutaton symbol, and summaton over k s mpled) Then t happens that S = h /4 for all = x, y, z, therefore S = S x + S y + S z = 3 h /4 and t s clear that [S, S x ] = 3 h [I, S x ]/4 = 0 When calculatng [S x, S y ] one notces that S x S y = S y S x whch mples that {S x, S y } = 0

4 (c) Snce matrces are handy objects, let us express the ladder operators Ŝ± also n the famlar { S z ;, S z ; } bass: Ŝ ± = Ŝx ± Ŝy S + = h ( ) h ( ) ( ) = h, S = h ( ) h ( ) ( ) = h, or Ŝ + = h S z ; S z ; Ŝ = h S z ; S z ; and operatons to Ŝz egenstates result: Ŝ + S z ; = 0 Ŝ S z ; = h S z ; Ŝ + S z ; = h S z ; Ŝ S z ; = 0 Now, the physcal meanng of the ladder operators can be read Operator Ŝ+ rases the spn component by h and f the spn component cannot be rased further, we get null state Smlarly, Ŝ lowers the spn component by h Both these operators are non-hermtan 4 Invarance of trace Prove the theorem from lecture note: If T s a untary matrx, then the matrces X and T XT have the same trace and the same egenvalues Soluton Trace of a matrx X s the sum of ts dagonal elements: Tr(X) = X and a remnder the matrx multplcaton expressed n ndex notaton goes (AB) j = k A kb kj Trace of the product then equals Tr AB = (AB) = A k B k = A k B k = Tr BA Wth ths n mnd we are ready to prove the trace nvarance k Tr(T XT ) = Tr(T T X) = Tr X Suppose x s an egenvector of X, wth an egenvalue x : Xx = x x, T T XT T x = x x, T XT [T x ] = x [T x ] k Insert I = T T n front and behnd X Multply both sdes by T from left Fnally, denotng T x = y we have an egenvalue equaton showng that y s an egenvector of T XT wth egenvalue x : Hence, x s also an egenvalue of T XT T XT y = x y 5 Pure ensemble In a pure ensemble, every state s the same, thus statstcal mechancs of such an ensemble reduces to ordnary quantum mechancs (b) Show that the relaton ˆρ = ρ holds for the densty operator of a pure ensemble, and thus Tr ˆρ = 1 Show that the correspondng matrx representaton s gven by a matrx where one of the dagonal elements s one and the rest are zero Is ths always true?

5 Soluton We defne the densty operator ˆρ as follows ˆρ = ω α () α () As stated n the text, ths operator contans all the physcally sgnfcant nformaton we can possbly obtan about the ensemble n queston, and the expectaton value of an observable  s gven by A pure ensemble s specfed by ω =  = Tr(ˆρÂ) { 1, for some = k 0, for k The correspondng densty operator can be wrtten as ρ = α () α () Clearly, the densty operator for a pure ensemble s dempotent, that s, ˆρ = α () α () α () α () = α () α () = ˆρ Takng the trace on both sdes obvously gves Tr ˆρ = Tr ˆρ = 1 Note that the calculaton goes backwards as well! Suppose we do not know whether or not ˆρ s a pure state densty operator, but we do know that Tr ˆρ = 1 Snce Tr ˆρ = 1 always, we have that ω = 1 (3) The square of ˆρ n the bass where t s dagonal s of course ˆρ = ω α () α (), and ts trace then becomes ω = 1 (4) Now make a counter clam: Suppose ˆρ s not pure, that s, ω < 1 for all But then we have that ω < ω for all, and so the sum of squares of ω must be less than the sum of ω Ths s n contradcton wth Eq (4), and so the counter assumpton s false Clearly then, at least one ω must be one, and snce ther sum s one, there can be only sngle for whch ths s true, say = k So, and ˆρ s pure ˆρ = α (k) α (k)

6 (b) The elements of the densty matrx take n ths case the form α () ˆρ α (j) = α () α (k) α (k) α (j) = δ k δ kj e the dagonal element correspondng to the sngle populated state (ndex k) s one whle all the other elements are zero But ths need not be always the case Although the densty operator has a representaton as a matrx wth a sngle one on the dagonal n the bass {α () }, n some other bass the representaton s not dagonal As an example, suppose n bass {β () }, we have so that ˆρ = α (k) α (k) α (k) = c 1 β (1) + c β (), = (c 1 β (1) + c β () )(c 1 β (1) + c β () ) = c 1 β (1) β (1) + c 1c β () β (1) + c 1 c β (1) β () + c β () β () In the new bass the densty matrx reads c 1 c 1 c 0 0 c 1c c 0 0 ρ = In general, the matrx ρ can be fully flled wth numbers, but stll have that ρ = ρ, Tr ρ = 1, and represent a pure ensemble

Quantum Mechanics for Scientists and Engineers. David Miller

Quantum Mechanics for Scientists and Engineers. David Miller Quantum Mechancs for Scentsts and Engneers Davd Mller Types of lnear operators Types of lnear operators Blnear expanson of operators Blnear expanson of lnear operators We know that we can expand functons

More information

PHYS 215C: Quantum Mechanics (Spring 2017) Problem Set 3 Solutions

PHYS 215C: Quantum Mechanics (Spring 2017) Problem Set 3 Solutions PHYS 5C: Quantum Mechancs Sprng 07 Problem Set 3 Solutons Prof. Matthew Fsher Solutons prepared by: Chatanya Murthy and James Sully June 4, 07 Please let me know f you encounter any typos n the solutons.

More information

ψ = i c i u i c i a i b i u i = i b 0 0 b 0 0

ψ = i c i u i c i a i b i u i = i b 0 0 b 0 0 Quantum Mechancs, Advanced Course FMFN/FYSN7 Solutons Sheet Soluton. Lets denote the two operators by  and ˆB, the set of egenstates by { u }, and the egenvalues as  u = a u and ˆB u = b u. Snce the

More information

1 Vectors over the complex numbers

1 Vectors over the complex numbers Vectors for quantum mechancs 1 D. E. Soper 2 Unversty of Oregon 5 October 2011 I offer here some background for Chapter 1 of J. J. Sakura, Modern Quantum Mechancs. 1 Vectors over the complex numbers What

More information

Quantum Mechanics I - Session 4

Quantum Mechanics I - Session 4 Quantum Mechancs I - Sesson 4 Aprl 3, 05 Contents Operators Change of Bass 4 3 Egenvectors and Egenvalues 5 3. Denton....................................... 5 3. Rotaton n D....................................

More information

Salmon: Lectures on partial differential equations. Consider the general linear, second-order PDE in the form. ,x 2

Salmon: Lectures on partial differential equations. Consider the general linear, second-order PDE in the form. ,x 2 Salmon: Lectures on partal dfferental equatons 5. Classfcaton of second-order equatons There are general methods for classfyng hgher-order partal dfferental equatons. One s very general (applyng even to

More information

Solution 1 for USTC class Physics of Quantum Information

Solution 1 for USTC class Physics of Quantum Information Soluton 1 for 018 019 USTC class Physcs of Quantum Informaton Shua Zhao, Xn-Yu Xu and Ka Chen Natonal Laboratory for Physcal Scences at Mcroscale and Department of Modern Physcs, Unversty of Scence and

More information

Homework Notes Week 7

Homework Notes Week 7 Homework Notes Week 7 Math 4 Sprng 4 #4 (a Complete the proof n example 5 that s an nner product (the Frobenus nner product on M n n (F In the example propertes (a and (d have already been verfed so we

More information

The Order Relation and Trace Inequalities for. Hermitian Operators

The Order Relation and Trace Inequalities for. Hermitian Operators Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence

More information

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1 C/CS/Phy9 Problem Set 3 Solutons Out: Oct, 8 Suppose you have two qubts n some arbtrary entangled state ψ You apply the teleportaton protocol to each of the qubts separately What s the resultng state obtaned

More information

Ph 219a/CS 219a. Exercises Due: Wednesday 23 October 2013

Ph 219a/CS 219a. Exercises Due: Wednesday 23 October 2013 1 Ph 219a/CS 219a Exercses Due: Wednesday 23 October 2013 1.1 How far apart are two quantum states? Consder two quantum states descrbed by densty operators ρ and ρ n an N-dmensonal Hlbert space, and consder

More information

Solution 1 for USTC class Physics of Quantum Information

Solution 1 for USTC class Physics of Quantum Information Soluton 1 for 017 018 USTC class Physcs of Quantum Informaton Shua Zhao, Xn-Yu Xu and Ka Chen Natonal Laboratory for Physcal Scences at Mcroscale and Department of Modern Physcs, Unversty of Scence and

More information

Advanced Quantum Mechanics

Advanced Quantum Mechanics Advanced Quantum Mechancs Rajdeep Sensarma! sensarma@theory.tfr.res.n ecture #9 QM of Relatvstc Partcles Recap of ast Class Scalar Felds and orentz nvarant actons Complex Scalar Feld and Charge conjugaton

More information

14 The Postulates of Quantum mechanics

14 The Postulates of Quantum mechanics 14 The Postulates of Quantum mechancs Postulate 1: The state of a system s descrbed completely n terms of a state vector Ψ(r, t), whch s quadratcally ntegrable. Postulate 2: To every physcally observable

More information

9 Characteristic classes

9 Characteristic classes THEODORE VORONOV DIFFERENTIAL GEOMETRY. Sprng 2009 [under constructon] 9 Characterstc classes 9.1 The frst Chern class of a lne bundle Consder a complex vector bundle E B of rank p. We shall construct

More information

A how to guide to second quantization method.

A how to guide to second quantization method. Phys. 67 (Graduate Quantum Mechancs Sprng 2009 Prof. Pu K. Lam. Verson 3 (4/3/2009 A how to gude to second quantzaton method. -> Second quantzaton s a mathematcal notaton desgned to handle dentcal partcle

More information

Lecture 6/7 (February 10/12, 2014) DIRAC EQUATION. The non-relativistic Schrödinger equation was obtained by noting that the Hamiltonian 2

Lecture 6/7 (February 10/12, 2014) DIRAC EQUATION. The non-relativistic Schrödinger equation was obtained by noting that the Hamiltonian 2 P470 Lecture 6/7 (February 10/1, 014) DIRAC EQUATION The non-relatvstc Schrödnger equaton was obtaned by notng that the Hamltonan H = P (1) m can be transformed nto an operator form wth the substtutons

More information

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

2.3 Nilpotent endomorphisms

2.3 Nilpotent endomorphisms s a block dagonal matrx, wth A Mat dm U (C) In fact, we can assume that B = B 1 B k, wth B an ordered bass of U, and that A = [f U ] B, where f U : U U s the restrcton of f to U 40 23 Nlpotent endomorphsms

More information

LECTURE 9 CANONICAL CORRELATION ANALYSIS

LECTURE 9 CANONICAL CORRELATION ANALYSIS LECURE 9 CANONICAL CORRELAION ANALYSIS Introducton he concept of canoncal correlaton arses when we want to quantfy the assocatons between two sets of varables. For example, suppose that the frst set of

More information

Representation theory and quantum mechanics tutorial Representation theory and quantum conservation laws

Representation theory and quantum mechanics tutorial Representation theory and quantum conservation laws Representaton theory and quantum mechancs tutoral Representaton theory and quantum conservaton laws Justn Campbell August 1, 2017 1 Generaltes on representaton theory 1.1 Let G GL m (R) be a real algebrac

More information

Singular Value Decomposition: Theory and Applications

Singular Value Decomposition: Theory and Applications Sngular Value Decomposton: Theory and Applcatons Danel Khashab Sprng 2015 Last Update: March 2, 2015 1 Introducton A = UDV where columns of U and V are orthonormal and matrx D s dagonal wth postve real

More information

Math 217 Fall 2013 Homework 2 Solutions

Math 217 Fall 2013 Homework 2 Solutions Math 17 Fall 013 Homework Solutons Due Thursday Sept. 6, 013 5pm Ths homework conssts of 6 problems of 5 ponts each. The total s 30. You need to fully justfy your answer prove that your functon ndeed has

More information

Density matrix. c α (t)φ α (q)

Density matrix. c α (t)φ α (q) Densty matrx Note: ths s supplementary materal. I strongly recommend that you read t for your own nterest. I beleve t wll help wth understandng the quantum ensembles, but t s not necessary to know t n

More information

2 More examples with details

2 More examples with details Physcs 129b Lecture 3 Caltech, 01/15/19 2 More examples wth detals 2.3 The permutaton group n = 4 S 4 contans 4! = 24 elements. One s the dentty e. Sx of them are exchange of two objects (, j) ( to j and

More information

Lecture Notes 7: The Unruh Effect

Lecture Notes 7: The Unruh Effect Quantum Feld Theory for Leg Spnners 17/1/11 Lecture Notes 7: The Unruh Effect Lecturer: Prakash Panangaden Scrbe: Shane Mansfeld 1 Defnng the Vacuum Recall from the last lecture that choosng a complex

More information

= = = (a) Use the MATLAB command rref to solve the system. (b) Let A be the coefficient matrix and B be the right-hand side of the system.

= = = (a) Use the MATLAB command rref to solve the system. (b) Let A be the coefficient matrix and B be the right-hand side of the system. Chapter Matlab Exercses Chapter Matlab Exercses. Consder the lnear system of Example n Secton.. x x x y z y y z (a) Use the MATLAB command rref to solve the system. (b) Let A be the coeffcent matrx and

More information

Causal Diamonds. M. Aghili, L. Bombelli, B. Pilgrim

Causal Diamonds. M. Aghili, L. Bombelli, B. Pilgrim Causal Damonds M. Aghl, L. Bombell, B. Plgrm Introducton The correcton to volume of a causal nterval due to curvature of spacetme has been done by Myrhem [] and recently by Gbbons & Solodukhn [] and later

More information

Lecture 5 Decoding Binary BCH Codes

Lecture 5 Decoding Binary BCH Codes Lecture 5 Decodng Bnary BCH Codes In ths class, we wll ntroduce dfferent methods for decodng BCH codes 51 Decodng the [15, 7, 5] 2 -BCH Code Consder the [15, 7, 5] 2 -code C we ntroduced n the last lecture

More information

MATH 241B FUNCTIONAL ANALYSIS - NOTES EXAMPLES OF C ALGEBRAS

MATH 241B FUNCTIONAL ANALYSIS - NOTES EXAMPLES OF C ALGEBRAS MATH 241B FUNCTIONAL ANALYSIS - NOTES EXAMPLES OF C ALGEBRAS These are nformal notes whch cover some of the materal whch s not n the course book. The man purpose s to gve a number of nontrval examples

More information

The Feynman path integral

The Feynman path integral The Feynman path ntegral Aprl 3, 205 Hesenberg and Schrödnger pctures The Schrödnger wave functon places the tme dependence of a physcal system n the state, ψ, t, where the state s a vector n Hlbert space

More information

EEE 241: Linear Systems

EEE 241: Linear Systems EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they

More information

Phys304 Quantum Physics II (2005) Quantum Mechanics Summary. 2. This kind of behaviour can be described in the mathematical language of vectors:

Phys304 Quantum Physics II (2005) Quantum Mechanics Summary. 2. This kind of behaviour can be described in the mathematical language of vectors: MACQUARIE UNIVERSITY Department of Physcs Dvson of ICS Phys304 Quantum Physcs II (2005) Quantum Mechancs Summary The followng defntons and concepts set up the basc mathematcal language used n quantum mechancs,

More information

First day August 1, Problems and Solutions

First day August 1, Problems and Solutions FOURTH INTERNATIONAL COMPETITION FOR UNIVERSITY STUDENTS IN MATHEMATICS July 30 August 4, 997, Plovdv, BULGARIA Frst day August, 997 Problems and Solutons Problem. Let {ε n } n= be a sequence of postve

More information

1 Matrix representations of canonical matrices

1 Matrix representations of canonical matrices 1 Matrx representatons of canoncal matrces 2-d rotaton around the orgn: ( ) cos θ sn θ R 0 = sn θ cos θ 3-d rotaton around the x-axs: R x = 1 0 0 0 cos θ sn θ 0 sn θ cos θ 3-d rotaton around the y-axs:

More information

The Prncpal Component Transform The Prncpal Component Transform s also called Karhunen-Loeve Transform (KLT, Hotellng Transform, oregenvector Transfor

The Prncpal Component Transform The Prncpal Component Transform s also called Karhunen-Loeve Transform (KLT, Hotellng Transform, oregenvector Transfor Prncpal Component Transform Multvarate Random Sgnals A real tme sgnal x(t can be consdered as a random process and ts samples x m (m =0; ;N, 1 a random vector: The mean vector of X s X =[x0; ;x N,1] T

More information

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016 U.C. Berkeley CS94: Spectral Methods and Expanders Handout 8 Luca Trevsan February 7, 06 Lecture 8: Spectral Algorthms Wrap-up In whch we talk about even more generalzatons of Cheeger s nequaltes, and

More information

Ph 219a/CS 219a. Exercises Due: Wednesday 12 November 2008

Ph 219a/CS 219a. Exercises Due: Wednesday 12 November 2008 1 Ph 19a/CS 19a Exercses Due: Wednesday 1 November 008.1 Whch state dd Alce make? Consder a game n whch Alce prepares one of two possble states: ether ρ 1 wth a pror probablty p 1, or ρ wth a pror probablty

More information

THEOREMS OF QUANTUM MECHANICS

THEOREMS OF QUANTUM MECHANICS THEOREMS OF QUANTUM MECHANICS In order to develop methods to treat many-electron systems (atoms & molecules), many of the theorems of quantum mechancs are useful. Useful Notaton The matrx element A mn

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

From Biot-Savart Law to Divergence of B (1)

From Biot-Savart Law to Divergence of B (1) From Bot-Savart Law to Dvergence of B (1) Let s prove that Bot-Savart gves us B (r ) = 0 for an arbtrary current densty. Frst take the dvergence of both sdes of Bot-Savart. The dervatve s wth respect to

More information

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011 Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected

More information

MEM 255 Introduction to Control Systems Review: Basics of Linear Algebra

MEM 255 Introduction to Control Systems Review: Basics of Linear Algebra MEM 255 Introducton to Control Systems Revew: Bascs of Lnear Algebra Harry G. Kwatny Department of Mechancal Engneerng & Mechancs Drexel Unversty Outlne Vectors Matrces MATLAB Advanced Topcs Vectors A

More information

arxiv: v2 [quant-ph] 29 Jun 2018

arxiv: v2 [quant-ph] 29 Jun 2018 Herarchy of Spn Operators, Quantum Gates, Entanglement, Tensor Product and Egenvalues Wll-Hans Steeb and Yorck Hardy arxv:59.7955v [quant-ph] 9 Jun 8 Internatonal School for Scentfc Computng, Unversty

More information

DISCRIMINANTS AND RAMIFIED PRIMES. 1. Introduction A prime number p is said to be ramified in a number field K if the prime ideal factorization

DISCRIMINANTS AND RAMIFIED PRIMES. 1. Introduction A prime number p is said to be ramified in a number field K if the prime ideal factorization DISCRIMINANTS AND RAMIFIED PRIMES KEITH CONRAD 1. Introducton A prme number p s sad to be ramfed n a number feld K f the prme deal factorzaton (1.1) (p) = po K = p e 1 1 peg g has some e greater than 1.

More information

Srednicki Chapter 34

Srednicki Chapter 34 Srednck Chapter 3 QFT Problems & Solutons A. George January 0, 203 Srednck 3.. Verfy that equaton 3.6 follows from equaton 3.. We take Λ = + δω: U + δω ψu + δω = + δωψ[ + δω] x Next we use equaton 3.3,

More information

Feb 14: Spatial analysis of data fields

Feb 14: Spatial analysis of data fields Feb 4: Spatal analyss of data felds Mappng rregularly sampled data onto a regular grd Many analyss technques for geophyscal data requre the data be located at regular ntervals n space and/or tme. hs s

More information

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis Resource Allocaton and Decson Analss (ECON 800) Sprng 04 Foundatons of Regresson Analss Readng: Regresson Analss (ECON 800 Coursepak, Page 3) Defntons and Concepts: Regresson Analss statstcal technques

More information

Solutions to exam in SF1811 Optimization, Jan 14, 2015

Solutions to exam in SF1811 Optimization, Jan 14, 2015 Solutons to exam n SF8 Optmzaton, Jan 4, 25 3 3 O------O -4 \ / \ / The network: \/ where all lnks go from left to rght. /\ / \ / \ 6 O------O -5 2 4.(a) Let x = ( x 3, x 4, x 23, x 24 ) T, where the varable

More information

Lecture 12: Discrete Laplacian

Lecture 12: Discrete Laplacian Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS

8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 493 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces you have studed thus far n the text are real vector spaces because the scalars

More information

Perron Vectors of an Irreducible Nonnegative Interval Matrix

Perron Vectors of an Irreducible Nonnegative Interval Matrix Perron Vectors of an Irreducble Nonnegatve Interval Matrx Jr Rohn August 4 2005 Abstract As s well known an rreducble nonnegatve matrx possesses a unquely determned Perron vector. As the man result of

More information

On the symmetric character of the thermal conductivity tensor

On the symmetric character of the thermal conductivity tensor On the symmetrc character of the thermal conductvty tensor Al R. Hadjesfandar Department of Mechancal and Aerospace Engneerng Unversty at Buffalo, State Unversty of New York Buffalo, NY 146 USA ah@buffalo.edu

More information

332600_08_1.qxp 4/17/08 11:29 AM Page 481

332600_08_1.qxp 4/17/08 11:29 AM Page 481 336_8_.qxp 4/7/8 :9 AM Page 48 8 Complex Vector Spaces 8. Complex Numbers 8. Conjugates and Dvson of Complex Numbers 8.3 Polar Form and DeMovre s Theorem 8.4 Complex Vector Spaces and Inner Products 8.5

More information

NOTES ON SIMPLIFICATION OF MATRICES

NOTES ON SIMPLIFICATION OF MATRICES NOTES ON SIMPLIFICATION OF MATRICES JONATHAN LUK These notes dscuss how to smplfy an (n n) matrx In partcular, we expand on some of the materal from the textbook (wth some repetton) Part of the exposton

More information

Solutions to Chapter 1 exercises

Solutions to Chapter 1 exercises Appendx S Solutons to Chapter exercses Soluton to Exercse.. Usng the result of Ex. A.5, we wrte don t forget the complex conjugaton where approprate! ψ ψ N alve ψ dead ψ N 4alve alve + alve dead dead alve

More information

e - c o m p a n i o n

e - c o m p a n i o n OPERATIONS RESEARCH http://dxdoorg/0287/opre007ec e - c o m p a n o n ONLY AVAILABLE IN ELECTRONIC FORM 202 INFORMS Electronc Companon Generalzed Quantty Competton for Multple Products and Loss of Effcency

More information

Lecture 10 Support Vector Machines II

Lecture 10 Support Vector Machines II Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed

More information

7. Products and matrix elements

7. Products and matrix elements 7. Products and matrx elements 1 7. Products and matrx elements Based on the propertes of group representatons, a number of useful results can be derved. Consder a vector space V wth an nner product ψ

More information

SL n (F ) Equals its Own Derived Group

SL n (F ) Equals its Own Derived Group Internatonal Journal of Algebra, Vol. 2, 2008, no. 12, 585-594 SL n (F ) Equals ts Own Derved Group Jorge Macel BMCC-The Cty Unversty of New York, CUNY 199 Chambers street, New York, NY 10007, USA macel@cms.nyu.edu

More information

EPR Paradox and the Physical Meaning of an Experiment in Quantum Mechanics. Vesselin C. Noninski

EPR Paradox and the Physical Meaning of an Experiment in Quantum Mechanics. Vesselin C. Noninski EPR Paradox and the Physcal Meanng of an Experment n Quantum Mechancs Vesseln C Nonnsk vesselnnonnsk@verzonnet Abstract It s shown that there s one purely determnstc outcome when measurement s made on

More information

REAL ANALYSIS I HOMEWORK 1

REAL ANALYSIS I HOMEWORK 1 REAL ANALYSIS I HOMEWORK CİHAN BAHRAN The questons are from Tao s text. Exercse 0.0.. If (x α ) α A s a collecton of numbers x α [0, + ] such that x α

More information

Solutions Homework 4 March 5, 2018

Solutions Homework 4 March 5, 2018 1 Solutons Homework 4 March 5, 018 Soluton to Exercse 5.1.8: Let a IR be a translaton and c > 0 be a re-scalng. ˆb1 (cx + a) cx n + a (cx 1 + a) c x n x 1 cˆb 1 (x), whch shows ˆb 1 s locaton nvarant and

More information

COMPLEX NUMBERS AND QUADRATIC EQUATIONS

COMPLEX NUMBERS AND QUADRATIC EQUATIONS COMPLEX NUMBERS AND QUADRATIC EQUATIONS INTRODUCTION We know that x 0 for all x R e the square of a real number (whether postve, negatve or ero) s non-negatve Hence the equatons x, x, x + 7 0 etc are not

More information

= a. a = Let us now study what is a? c ( a A a )

= a. a = Let us now study what is a? c ( a A a ) 7636S ADVANCED QUANTUM MECHANICS Solutions 1 Spring 010 1 Warm up a Show that the eigenvalues of a Hermitian operator A are real and that the eigenkets of A corresponding to dierent eigenvalues are orthogonal

More information

CSCE 790S Background Results

CSCE 790S Background Results CSCE 790S Background Results Stephen A. Fenner September 8, 011 Abstract These results are background to the course CSCE 790S/CSCE 790B, Quantum Computaton and Informaton (Sprng 007 and Fall 011). Each

More information

Problem Do any of the following determine homomorphisms from GL n (C) to GL n (C)?

Problem Do any of the following determine homomorphisms from GL n (C) to GL n (C)? Homework 8 solutons. Problem 16.1. Whch of the followng defne homomomorphsms from C\{0} to C\{0}? Answer. a) f 1 : z z Yes, f 1 s a homomorphsm. We have that z s the complex conjugate of z. If z 1,z 2

More information

Lecture 4: Constant Time SVD Approximation

Lecture 4: Constant Time SVD Approximation Spectral Algorthms and Representatons eb. 17, Mar. 3 and 8, 005 Lecture 4: Constant Tme SVD Approxmaton Lecturer: Santosh Vempala Scrbe: Jangzhuo Chen Ths topc conssts of three lectures 0/17, 03/03, 03/08),

More information

APPENDIX A Some Linear Algebra

APPENDIX A Some Linear Algebra APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

Random Walks on Digraphs

Random Walks on Digraphs Random Walks on Dgraphs J. J. P. Veerman October 23, 27 Introducton Let V = {, n} be a vertex set and S a non-negatve row-stochastc matrx (.e. rows sum to ). V and S defne a dgraph G = G(V, S) and a drected

More information

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look

More information

Algebraic Solutions to Multidimensional Minimax Location Problems with Chebyshev Distance

Algebraic Solutions to Multidimensional Minimax Location Problems with Chebyshev Distance Recent Researches n Appled and Computatonal Mathematcs Algebrac Solutons to Multdmensonal Mnmax Locaton Problems wth Chebyshev Dstance NIKOLAI KRIVULIN Faculty of Mathematcs and Mechancs St Petersburg

More information

Quantum Mechanics for Scientists and Engineers

Quantum Mechanics for Scientists and Engineers Quantu Mechancs or Scentsts and Engneers Sangn K Advanced Coputatonal Electroagnetcs Lab redkd@yonse.ac.kr Nov. 4 th, 26 Outlne Quantu Mechancs or Scentsts and Engneers Blnear expanson o lnear operators

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

CHALMERS, GÖTEBORGS UNIVERSITET. SOLUTIONS to RE-EXAM for ARTIFICIAL NEURAL NETWORKS. COURSE CODES: FFR 135, FIM 720 GU, PhD

CHALMERS, GÖTEBORGS UNIVERSITET. SOLUTIONS to RE-EXAM for ARTIFICIAL NEURAL NETWORKS. COURSE CODES: FFR 135, FIM 720 GU, PhD CHALMERS, GÖTEBORGS UNIVERSITET SOLUTIONS to RE-EXAM for ARTIFICIAL NEURAL NETWORKS COURSE CODES: FFR 35, FIM 72 GU, PhD Tme: Place: Teachers: Allowed materal: Not allowed: January 2, 28, at 8 3 2 3 SB

More information

Group Theory in Physics

Group Theory in Physics Group Theory n Physcs Lng-Fong L (Insttute) Representaton Theory / 4 Theory of Group Representaton In physcal applcaton, group representaton s mportant n deducng the consequence of the symmetres of the

More information

Poisson brackets and canonical transformations

Poisson brackets and canonical transformations rof O B Wrght Mechancs Notes osson brackets and canoncal transformatons osson Brackets Consder an arbtrary functon f f ( qp t) df f f f q p q p t But q p p where ( qp ) pq q df f f f p q q p t In order

More information

MATH Sensitivity of Eigenvalue Problems

MATH Sensitivity of Eigenvalue Problems MATH 537- Senstvty of Egenvalue Problems Prelmnares Let A be an n n matrx, and let λ be an egenvalue of A, correspondngly there are vectors x and y such that Ax = λx and y H A = λy H Then x s called A

More information

Spin. Introduction. Michael Fowler 11/26/06

Spin. Introduction. Michael Fowler 11/26/06 Spn Mchael Fowler /6/6 Introducton The Stern Gerlach experment for the smplest possble atom, hydrogen n ts ground state, demonstrated unambguously that the component of the magnetc moment of the atom along

More information

Appendix for Causal Interaction in Factorial Experiments: Application to Conjoint Analysis

Appendix for Causal Interaction in Factorial Experiments: Application to Conjoint Analysis A Appendx for Causal Interacton n Factoral Experments: Applcaton to Conjont Analyss Mathematcal Appendx: Proofs of Theorems A. Lemmas Below, we descrbe all the lemmas, whch are used to prove the man theorems

More information

Workshop: Approximating energies and wave functions Quantum aspects of physical chemistry

Workshop: Approximating energies and wave functions Quantum aspects of physical chemistry Workshop: Approxmatng energes and wave functons Quantum aspects of physcal chemstry http://quantum.bu.edu/pltl/6/6.pdf Last updated Thursday, November 7, 25 7:9:5-5: Copyrght 25 Dan Dll (dan@bu.edu) Department

More information

Lecture 10: May 6, 2013

Lecture 10: May 6, 2013 TTIC/CMSC 31150 Mathematcal Toolkt Sprng 013 Madhur Tulsan Lecture 10: May 6, 013 Scrbe: Wenje Luo In today s lecture, we manly talked about random walk on graphs and ntroduce the concept of graph expander,

More information

Errors for Linear Systems

Errors for Linear Systems Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch

More information

Bernoulli Numbers and Polynomials

Bernoulli Numbers and Polynomials Bernoull Numbers and Polynomals T. Muthukumar tmk@tk.ac.n 17 Jun 2014 The sum of frst n natural numbers 1, 2, 3,..., n s n n(n + 1 S 1 (n := m = = n2 2 2 + n 2. Ths formula can be derved by notng that

More information

Mathematical Preparations

Mathematical Preparations 1 Introducton Mathematcal Preparatons The theory of relatvty was developed to explan experments whch studed the propagaton of electromagnetc radaton n movng coordnate systems. Wthn expermental error the

More information

However, since P is a symmetric idempotent matrix, of P are either 0 or 1 [Eigen-values

However, since P is a symmetric idempotent matrix, of P are either 0 or 1 [Eigen-values Fall 007 Soluton to Mdterm Examnaton STAT 7 Dr. Goel. [0 ponts] For the general lnear model = X + ε, wth uncorrelated errors havng mean zero and varance σ, suppose that the desgn matrx X s not necessarly

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

8.6 The Complex Number System

8.6 The Complex Number System 8.6 The Complex Number System Earler n the chapter, we mentoned that we cannot have a negatve under a square root, snce the square of any postve or negatve number s always postve. In ths secton we want

More information

Dynamic Programming. Preview. Dynamic Programming. Dynamic Programming. Dynamic Programming (Example: Fibonacci Sequence)

Dynamic Programming. Preview. Dynamic Programming. Dynamic Programming. Dynamic Programming (Example: Fibonacci Sequence) /24/27 Prevew Fbonacc Sequence Longest Common Subsequence Dynamc programmng s a method for solvng complex problems by breakng them down nto smpler sub-problems. It s applcable to problems exhbtng the propertes

More information

Hidden Markov Models & The Multivariate Gaussian (10/26/04)

Hidden Markov Models & The Multivariate Gaussian (10/26/04) CS281A/Stat241A: Statstcal Learnng Theory Hdden Markov Models & The Multvarate Gaussan (10/26/04) Lecturer: Mchael I. Jordan Scrbes: Jonathan W. Hu 1 Hdden Markov Models As a bref revew, hdden Markov models

More information

THE VIBRATIONS OF MOLECULES II THE CARBON DIOXIDE MOLECULE Student Instructions

THE VIBRATIONS OF MOLECULES II THE CARBON DIOXIDE MOLECULE Student Instructions THE VIBRATIONS OF MOLECULES II THE CARBON DIOXIDE MOLECULE Student Instructons by George Hardgrove Chemstry Department St. Olaf College Northfeld, MN 55057 hardgrov@lars.acc.stolaf.edu Copyrght George

More information

Matrix Approximation via Sampling, Subspace Embedding. 1 Solving Linear Systems Using SVD

Matrix Approximation via Sampling, Subspace Embedding. 1 Solving Linear Systems Using SVD Matrx Approxmaton va Samplng, Subspace Embeddng Lecturer: Anup Rao Scrbe: Rashth Sharma, Peng Zhang 0/01/016 1 Solvng Lnear Systems Usng SVD Two applcatons of SVD have been covered so far. Today we loo

More information

MMA and GCMMA two methods for nonlinear optimization

MMA and GCMMA two methods for nonlinear optimization MMA and GCMMA two methods for nonlnear optmzaton Krster Svanberg Optmzaton and Systems Theory, KTH, Stockholm, Sweden. krlle@math.kth.se Ths note descrbes the algorthms used n the author s 2007 mplementatons

More information

The Dirac Equation for a One-electron atom. In this section we will derive the Dirac equation for a one-electron atom.

The Dirac Equation for a One-electron atom. In this section we will derive the Dirac equation for a one-electron atom. The Drac Equaton for a One-electron atom In ths secton we wll derve the Drac equaton for a one-electron atom. Accordng to Ensten the energy of a artcle wth rest mass m movng wth a velocty V s gven by E

More information

Supplementary Information

Supplementary Information Supplementary Informaton Quantum correlatons wth no causal order Ognyan Oreshkov 2 Fabo Costa Časlav Brukner 3 Faculty of Physcs Unversty of Venna Boltzmanngasse 5 A-090 Venna Austra. 2 QuIC Ecole Polytechnque

More information

The exponential map of GL(N)

The exponential map of GL(N) The exponental map of GLN arxv:hep-th/9604049v 9 Apr 996 Alexander Laufer Department of physcs Unversty of Konstanz P.O. 5560 M 678 78434 KONSTANZ Aprl 9, 996 Abstract A fnte expanson of the exponental

More information

BOUNDEDNESS OF THE RIESZ TRANSFORM WITH MATRIX A 2 WEIGHTS

BOUNDEDNESS OF THE RIESZ TRANSFORM WITH MATRIX A 2 WEIGHTS BOUNDEDNESS OF THE IESZ TANSFOM WITH MATIX A WEIGHTS Introducton Let L = L ( n, be the functon space wth norm (ˆ f L = f(x C dx d < For a d d matrx valued functon W : wth W (x postve sem-defnte for all

More information