Why are random matrix eigenvalues cool - PowerPoint PPT Presentation

About This Presentation
Title:

Why are random matrix eigenvalues cool

Description:

Ingredient: Take Any important mathematics. Then Randomize! This will have ... The classical & most famous rand eig theorem. Let S = random symmetric Gaussian ... – PowerPoint PPT presentation

Number of Views:125
Avg rating:3.0/5.0
Slides: 86
Provided by: wwwma
Learn more at: https://math.mit.edu
Category:

less

Transcript and Presenter's Notes

Title: Why are random matrix eigenvalues cool


1
Why are random matrix eigenvalues cool?
  • Alan Edelman
  • MIT Dept of Mathematics,
  • Lab for Computer Science
  • MAA Mathfest 2002
  • Thursday, August 1

2
Message
  • Ingredient Take Any important mathematics
  • Then Randomize!
  • This will have many applications!

3
Some fun tidbits
  • The circular law
  • The semi-circular law
  • Infinite vs finite
  • How many are real?
  • Stochastic Numerical Algorithms
  • Condition Numbers
  • Small networks
  • Riemann Zeta Function
  • Matrix Jacobians

4
Girkos Circular Law, n2000
Has anyone studied spacings?
5
Wigners Semi-Circle
  • The classical most famous rand eig theorem
  • Let S random symmetric Gaussian
  • MATLAB Arandn(n) S(AA)/2
  • Normalized eigenvalue histogram is a semi-circle
  • Precise statements require n?? etc.

6
Wigners Semi-Circle
  • The classical most famous rand eig theorem
  • Let S random symmetric Gaussian
  • MATLAB Arandn(n) S(AA)/2
  • Normalized eigenvalue histogram is a semi-circle
  • Precise statements require n?? etc.

n20 s30000 d.05 matrix size, samples,
sample dist e gather up
eigenvalues im1 imaginary(1) or
real(0) for i1s, arandn(n)imsqrt(-1)randn(
n)a(aa')/(2sqrt(2n(im1))) veig(a)'
ee v end hold off m xhist(e,-1.5d1.5)
bar(x,mpi/(2dns)) axis('square') axis(-1.5
1.5 -1 2) hold on t-1.011
plot(t,sqrt(1-t.2),'r')
7
Elements of Wigners Proof
  • Compute E(A2k)11 mean(?2k) (2k)th moment
  • Verify that the semicircle is the only
    distribution with these moments
  • (A2k)11 ?A1xAxyAwzAz1 paths of length 2k
  • Need only count number of special paths of length
    2k on k objects (all other terms 0 or
    negligible!)
  • This is a Catalan Number!

8
Catalan Numbers
  • ways to parenthesize (n1)
    objects
  • Matrix Power Term Graph
  • (1((23)4)) A12A23A32A24A42A21
  • (((12)3)4) A12A21A13A31A14A41
  • (1(2(34))) A12A23A34A43A32A21
  • ((12)(34)) A12A21A13A34A43A31
  • ((1(23))4) A12A23A32A21A14A41
  • number of special paths on n departing from 1
    once
  • Pass 1, (loadadvance, multiplyretreat), Return
    to 1

9
Finite Versions
  • n2 n4
  • n3 n5

10
How many eigenvalues of a random matrix are real?
gtgt eeig(randn(7)) e 1.9771
1.3442 0.6316 -1.1664
1.3504i -1.1664 - 1.3504i -2.1461 0.7288i
-2.1461 - 0.7288i
gtgt eeig(randn(7)) e -2.0767 1.1992i
-2.0767 - 1.1992i 2.9437 0.0234
0.4845i 0.0234 - 0.4845i 1.1914 0.3629i
1.1914 - 0.3629i
gtgt eeig(randn(7)) e -2.1633
-0.9264 -0. 3283 2.5242 1.6230
0.9011i 1.6230 - 0.9011i 0.5467
3 real 1 real
5 real
7x7 random Gaussian
11
How many eigenvalues of a random matrix are real?
n7
0.00069 0.08460 0.57727 0.33744
7 reals 5 reals 3 reals 1 real
12
How many eigenvalues of a random matrix are real?
n7
0.00069 0.08460 0.57727 0.33744
7 reals 5 reals 3 reals 1 real
These are exact but hard to compute! New research
suggests a Jack polynomial solution.
13
How many eigenvalues of a random matrix are real?
  • The Probability that a matrix has all real
    eigenvalues is exactly
  • Pn,n2-n(n-1)/4
  • Proof based on Schur Form

14
Gram Schmidt (or QR) Stochastically
  • Gram Schmidt
  • Orthogonal Transformations to Upper
    Triangular Form
  • A Q R (orthog upper triangular)

15
Orthogonal Invariance of Gaussians
Qrandn(n,1) ? randn(n,1) If Q orthogonal
Q?
16
Orthogonal Invariance
Qrandn(n,1) ? randn(n,1) If Q orthogonal
Q?
17
Chi Distribution
norm(randn(n,1)) ? ?n
?n
18
Chi Distribution
norm(randn(n,1)) ? ?n
?n
19
Chi Distribution
norm(randn(n,1)) ? ?n
?n
n need not be integer
20
(No Transcript)
21
(No Transcript)
22
(No Transcript)
23
(No Transcript)
24
(No Transcript)
25
(No Transcript)
26
(No Transcript)
27
(No Transcript)
28
(No Transcript)
29
(No Transcript)
30
(No Transcript)
31
(No Transcript)
32
(No Transcript)
33
(No Transcript)
34
(No Transcript)
35
(No Transcript)
36
(No Transcript)
37
(No Transcript)
38
(No Transcript)
39
(No Transcript)
40
(No Transcript)
41
(No Transcript)
42
(No Transcript)
43
(No Transcript)
44
(No Transcript)
45
(No Transcript)
46
(No Transcript)
47
(No Transcript)
48
(No Transcript)
49
(No Transcript)
50
(No Transcript)
51
(No Transcript)
52
(No Transcript)
53
(No Transcript)
54
(No Transcript)
55
(No Transcript)
56
(No Transcript)
57
(No Transcript)
58
(No Transcript)
59
(No Transcript)
60
(No Transcript)
61
(No Transcript)
62
(No Transcript)
63
(No Transcript)
64
(No Transcript)
65
(No Transcript)
66
(No Transcript)
67
(No Transcript)
68
(No Transcript)
69
Same idea sym matrix to tridiagonal form
Same eigenvalue distribution as GOE O(n)
storage !! O(n) computation (potentially)
70
Same idea General beta
beta 1 reals 2 complexes 4 quaternions
Bidiagonal Version corresponds To Wishart
matrices of Statistics
71
Numerical Analysis Condition Numbers
  • ?(A) condition number of A
  • If AU?V is the svd, then ?(A) ?max/?min .
  • Alternatively, ?(A) ?? max (AA)/?? min (AA)
  • One number that measures digits lost in finite
    precision and general matrix badness
  • Smallgood
  • Largebad
  • The condition of a random matrix???

72
Von Neumann co.
  • Solve Axb via x (AA) -1A b
  • M ?A-1
  • Matrix Residual AM-I2
  • AM-I2lt 200?2 n ?
  • How should we estimate ??
  • Assume, as a model, that the elements of A are
    independent standard normals!

?
73
Von Neumann co. estimates (1947-1951)
  • For a random matrix of order n the expectation
    value has been shown to be about n
  • Goldstine, von Neumann
  • we choose two different values of ?, namely n
    and ?10n
  • Bargmann, Montgomery, vN
  • With a probability 1 ? lt 10n
  • Goldstine, von Neumann

X ?
74
Random cond numbers, n??
Distribution of ?/n
Experiment with n200
75
Finite n
  • n10
    n25
  • n50
    n100

76
Small World Networks 6 degrees of separation
  • Edelman, Eriksson, Strang
  • Eigenvalues of ATPTP, Prandperm(n)
  • Incidence
    matrix of graph with two
  • superimposed cycles.

77
Small World Networks 6 degrees of separation
  • Edelman, Eriksson, Strang
  • Eigenvalues of ATPTP, Prandperm(n)
  • Incidence
    matrix of graph with two
  • superimposed cycles.
  • Wigner style derivation counts number of paths on
    a tree starting and ending at the same point
    (tree no accidents!) (McKay)
  • We first discovered the formula using the
    superseeker
  • Catalan number answer d2n-1-? d2j-1(d-1)n-j1Cn-j

78
The Riemann Zeta Function
On the real line with xgt1, for example
May be analytically extended to the complex
plane, with singularity only at x1.
79
The Riemann Hypothesis
-3 -2 -1 0 ½ 1
2 3
All nontrivial roots of ?(x) satisfy
Re(x)1/2. (Trivial roots at negative even
integers.)
80
The Riemann Hypothesis
Zeros .5i 14.134725142 21.022039639
25.010857580 30.424876126 32.935061588
37.586178159 40.918719012 43.327073281
48.005150881 49.773832478 52.970321478
56.446247697 59.347044003
?(x) along Re(x)1/2
-3 -2 -1 0 ½ 1
2 3
All nontrivial roots of ?(x) satisfy
Re(x)1/2. (Trivial roots at negative even
integers.)
81
Computation of Zeros
  • Odlyzkos fantastic computation of 10k1
    through 10k10,000 for k12,21,22.
  • See http//www.research.att.com/amo/zeta_tables/
  • Spacings behave like the eigenvalues of
  • Arandn(n)irandn(n) S(AA)/2

82
Nearest Neighbor Spacings Pairwise Correlation
Functions
83
Painlevé Equations
84
Spacings
  • Take a large collection of consecutive
    zeros/eigenvalues.
  • Normalize so that average spacing 1.
  • Spacing Function Histogram of consecutive
    differences (the (k1)st the kth)
  • Pairwise Correlation Function Histogram of all
    possible differences (the kth the jth)
  • Conjecture These functions are the same for
    random matrices and Riemann zeta

85
Some fun tidbits
  • The circular law
  • The semi-circular law
  • Infinite vs finite
  • How many are real?
  • Stochastic Numerical Algorithms
  • Condition Numbers
  • Small networks
  • Riemann Zeta Function
  • Matrix Jacobians

86
Matrix Factorization Jacobians
General
ALU AU?VT AX?X-1
AQR AQS (polar)
? uiin-i
? riim-i
? (?i2- ?j2)
? (?i?j)
? (?i-?j)2
Sym
Orthogonal
?sin(?i ?j)sin (?i- ?j)
Tridiagonal
TQ?QT
? (ti1,i)/ ?qi
87
Why cool?
  • Why is numerical linear algebra cool?
  • Mixture of theory and applications
  • Touches many topics
  • Easy to jump in to, but can spend a lifetime
    studying researching
  • Tons of activity in many areas
  • Mathematics Combinatorics, Harmonic Analysis,
    Integral Equations, Probability, Number Theory
  • Applied Math Chaotic Systems, Statistical
    Mechanics, Communications Theory, Radar Tracking,
    Nuclear Physics
  • Applications
  • BIG HUGE SUBJECT!!
Write a Comment
User Comments (0)
About PowerShow.com