Constructing and (some) classification of integer matrices with integer eigenvalues
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1 Constructing and (some) classification of integer matrices with integer eigenvalues Chris Towse* and Eric Campbell Scripps College Pomona College January 6, 2017
2 The question An example Solve a linear system: x (t) = Ax
3 The question An example Solve a linear system: x (t) = Ax Answer: x = C 1 v 1 e λ 1t + C 2 v 2 e λ 2t
4 The question An example Solve a linear system: x (t) = Ax Answer: x = C 1 v 1 e λ 1t + C 2 v 2 e λ 2t + + C n v n e λnt... or something similar.
5 The question An example An example: A =
6 The question An example An example: Eigenvalues are λ = 1, 1, A =
7 The question An example An example: Eigenvalues are λ = 1, 1, A = So the characteristic polynomial is χ A (x) = x 3 7x x 5.
8 The question An example An example: Eigenvalues are λ = 1, 1, A = So the characteristic polynomial is χ A (x) = x 3 7x x 5. Reasonable?
9 Set-up GOAL: Construct a matrix A with relatively small integer entries and with relatively small integer eigenvalues. (IMIE) Such an A will be a good example.
10 Set-up GOAL: Construct a matrix A with relatively small integer entries and with relatively small integer eigenvalues. (IMIE) Such an A will be a good example. Note: Martin and Wong, Almost all integer matrices have no integer eigenvalues
11 Set-up Recall/notation: where A = PDP 1 P = u 1 u 2 u n has columns which form a basis of eigenvectors for A and D = λ 1, λ 2,..., λ n is the diagonal matrix with corresponding eigenvalues on the diagonal.
12 Set-up Our example: A = D = 1, 1, 5 And in this case, P = A = PDP 1.
13 First approach Failure Idea: With the same P, try PDP 1 for D = 1, 3, 5.
14 First approach Failure Idea: With the same P, try PDP 1 for D = 1, 3, 5. Then /2 3 1/2 P P 1 = /2 1 7/2
15 First approach Failure The problem is that P 1 = (1/4)
16 First approach Failure The problem is that P 1 = (1/4) = 1 det P Padj Or, really, that det P = 4.
17 First approach Failure Solutions:
18 First approach Failure Solutions: 1. Choose P with det P = ±1. (New problem!)
19 First approach Failure Solutions: 1. Choose P with det P = ±1. (New problem!) 2. If k = det P, choose eigenvalues that are all multiples of k. (But probably avoid 0.)
20 First approach Failure Solutions: 1. Choose P with det P = ±1. (New problem!) 2. If k = det P, choose eigenvalues that are all multiples of k. (But probably avoid 0.) But actually...
21 First approach Improvement: modulo k Proposition (Eigenvalues congruent to b modulo k) Let P be an n n invertible matrix with k = det P 0. Suppose every λ i b mod k. (So all the λ i s are congruent to each other.) Then A = P λ 1,..., λ n P 1 is integral.
22 First approach Improvement: modulo k Proposition (Eigenvalues congruent to b modulo k) Let P be an n n invertible matrix with k = det P 0. Suppose every λ i b mod k. (So all the λ i s are congruent to each other.) Then A = P λ 1,..., λ n P 1 is integral. For example, P 1, 3, 5 P 1 =
23 First approach Improvement: modulo k Proposition (Eigenvalues congruent to b modulo k) Let P be an n n invertible matrix with k = det P 0. Suppose every λ i b mod k. (So all the λ i s are congruent to each other.) Then A = P λ 1,..., λ n P 1 is integral. For example, P 1, 3, 5 P 1 = which has χ(x) = x 3 3x 2 13x + 15 = (x 1)(x + 3)(x 5).
24 First approach Improvement: modulo k Proposition (Eigenvalues congruent to b modulo k) Let P be an n n invertible matrix with k = det P 0. Suppose every λ i b mod k. (So all the λ i s are congruent to each other.) Then A = P λ 1,..., λ n P 1 is integral. For example, P 1, 3, 5 P 1 = which has χ(x) = x 3 3x 2 13x + 15 = (x 1)(x + 3)(x 5). Good?
25 First approach Improvement: modulo k Eric Campbell (Pomona) created a web app
26 First approach Improvement: modulo k Eric Campbell (Pomona) created a web app matrices/
27 First approach Improvement: modulo k Eric Campbell (Pomona) created a web app matrices/ Input: eigenvectors (really P) Select good eigenvalues Output: IMIE and its characteristic polynomial
28 A different approach
29 A different approach A useful property Proposition If X is invertible than XY and YX have the same characteristic polynomials.
30 A different approach A useful property Proposition If X is invertible than XY and YX have the same characteristic polynomials. Idea [Renaud, 1983]: Take X, Y integer matrices. If YX is upper (or lower) triangular then the eigenvalues are the diagonal elements, hence integers. So XY will be a good example.
31 A different approach A useful property Proposition If X is invertible than XY and YX have the same characteristic polynomials. Idea [Renaud, 1983]: Take X, Y integer matrices. If YX is upper (or lower) triangular then the eigenvalues are the diagonal elements, hence integers. So XY will be a good example. If X and Y are n n, we are imposing n(n 1)/2 othogonality conditions on the rows of Y and columns of X.
32 A different approach A useful property ( ) ( ) Let X = and Y = ( ) 7 4 Then XY = has the same eigenvalues as 3 4 ( ) 5 YX =, namely λ = 5,
33 A different approach A useful property ( ) ( ) Let X = and Y = ( ) 7 4 Then XY = has the same eigenvalues as 3 4 ( ) 5 YX =, namely λ = 5, Starting with X, the only condition on Y is that the second row of Y must be orthogonal to the first row of X.
34 A different approach A better version Better fact: Theorem (Folk Theorem) Let U be an r s matrix and V be an s r matrix, where r s. Then χ UV (x) = x s r χ VU (x).
35 A different approach A better version Better fact: Theorem (Folk Theorem) Let U be an r s matrix and V be an s r matrix, where r s. Then χ UV (x) = x s r χ VU (x). Proof ( idea ) (due ( to Horn ) and Johnson): UV V 0 V VU
36 A different approach A better version Better fact: Theorem (Folk Theorem) Let U be an r s matrix and V be an s r matrix, where r s. Then χ UV (x) = x s r χ VU (x). Proof ( idea ) (due ( to Horn ) and Johnson): UV V 0 V VU ( ) Ir U via. 0 I s
37 A different approach A better version In other words, UV and VU have nearly the same characteristic polynomials and nearly the same eigenvalues. The only difference: 0 is a eigenvalue of the larger matrix, repeated as necessary. For now, let us take U to be n (n 1) and V to be (n 1) n.
38 A different approach A better version An example. Begin with U = V =
39 A different approach A better version Then VU =
40 A different approach A better version Then with eigenvalues λ = 2, 3, VU =
41 A different approach A better version Then with eigenvalues λ = 2, 3, 2 and VU = UV = So UV is an integer matrix with eigenvalues λ = 2, 3, 2
42 A different approach A better version Then with eigenvalues λ = 2, 3, 2 and VU = UV = So UV is an integer matrix with eigenvalues λ = 2, 3, 2 and λ = 0.
43 A different approach A better version Writing U = u 1 u 2 u 3 v 1 V = v 2 v 3
44 A different approach A better version Writing U = u 1 u 2 u 3 v 1 V = v 2 v 3 We needed v 2 u 1 = v 3 u 1 = v 3 u 2 = 0.
45 A different approach A better version Writing U = u 1 u 2 u 3 v 1 V = v 2 v 3 We needed v 2 u 1 = v 3 u 1 = v 3 u 2 = 0. Then the eigenvalues of UV are the three dot products v i u i and 0.
46 A different approach A better version Note: we now only need an (n 1) (n 1) matrix to be triangular. There are now (n 1)(n 2)/2 orthogonality conditions.
47 A different approach A better version Note: we now only need an (n 1) (n 1) matrix to be triangular. There are now (n 1)(n 2)/2 orthogonality conditions. This means there are no conditions to be checked in order to construct a 2 2 example.
48 A different approach A better version Any ( u1 ) ( ) (v1 ) u1 v v 2 = 1 u 1 v 2 u 2 u 2 v 1 u 2 v 2 is an integer matrix with eigenvalues 0 and u 1 v 1 + u 2 v 2.
49 A different approach A better version Any ( u1 ) ( ) (v1 ) u1 v v 2 = 1 u 1 v 2 u 2 u 2 v 1 u 2 v 2 is an integer matrix with eigenvalues 0 and u 1 v 1 + u 2 v 2. E.g. ( ) ( ) 2 (1 ) = λ = 2, 0
50 A different approach A better version Disadvantages: We always get 0 as an eigenvalue. What are the eigenvectors? Advantage: This is (nearly) a complete characterization for these matrices.
51 A different approach A shift by b Great tool No. 2:
52 A different approach A shift by b Great tool No. 2: Proposition (Shift Proposition) If A has eigenvalues λ 1,..., λ n.
53 A different approach A shift by b Great tool No. 2: Proposition (Shift Proposition) If A has eigenvalues λ 1,..., λ n. Then A + bi n has eigenvalues b + λ 1,..., b + λ n.
54 A different approach A shift by b Great tool No. 2: Proposition (Shift Proposition) If A has eigenvalues λ 1,..., λ n. Then A + bi n has eigenvalues b + λ 1,..., b + λ n. Why does this work? Note that A and A + bi have the same eigenvectors.
55 A different approach A shift by b Example: VU = so UV = has eigenvalues 2, 3, 2, 0.
56 A different approach A shift by b Example: VU = so UV = has eigenvalues 2, 3, 2, 0. So A = UV I = has eigenvalues 1, 4, 1,
57 A different approach A shift by b Earlier we had UV = ( ) ( ) 2 (1 ) = with λ = 2, 0
58 A different approach A shift by b Earlier we had UV = ( ) ( ) 2 (1 ) = with λ = 2, 0 So has eigenvalues λ = 1, 1. UV + I = ( )
59 A different approach A shift by b Note: This gives a quick proof to our Eigenvalues Congruent to b modulo k Proposition.
60 Classification Results
61 Classification Results This is actually a classification of all IMIEs. Theorem (Renaud) Every n n integer matrix with integer eigenvalues can be written as a UV + bi n where VU is a triangular (n 1) (n 1) matrix.
62 Classification Results This is actually a classification of all IMIEs. Theorem (Renaud) Every n n integer matrix with integer eigenvalues can be written as a UV + bi n where VU is a triangular (n 1) (n 1) matrix. Note: U is n (n 1) and V is (n 1) n.
63 Classification Results Further refinements Is there any way to take further advantage of the Folk Theorem?
64 Classification Results Further refinements Is there any way to take further advantage of the Folk Theorem? What if U is n r and V is r n?
65 Classification Results Further refinements Is there any way to take further advantage of the Folk Theorem? What if U is n r and V is r n? In particular, if U is n 1 and V is 1 n then VU is trivially triangular.
66 Classification Results Further refinements 1 Take U = 1 and V = ( ). 1 Then VU = (4) and UV must be a 3 3 integer matrix with eigenvalues 4, 0, 0.
67 Classification Results Further refinements 1 Take U = 1 and V = ( ). 1 Then VU = (4) and UV must be a 3 3 integer matrix with eigenvalues 4, 0, In fact UV + I 3 = = A, our original example
68 Classification Results Further refinements
69 Classification Results Further refinements What about the eigenvectors of UV?
70 Classification Results Further refinements What about the eigenvectors of UV? Suppose VU is not just triangular, but diagonal.
71 Classification Results Further refinements What about the eigenvectors of UV? Suppose VU is not just triangular, but diagonal. Then the (n 1) columns of U are eigenvectors corresponding to the λ 1,..., λ n 1.
72 Classification Results Further refinements What about the eigenvectors of UV? Suppose VU is not just triangular, but diagonal. Then the (n 1) columns of U are eigenvectors corresponding to the λ 1,..., λ n 1. Any vector, w in the null space of V is an eigenvector corresponding to b.
73 Classification Results Further refinements 3 2 Example. U = 1 0 and V = 1 2 ( )
74 Classification Results Further refinements 3 2 ( ) Example. U = and V = ( ) 4 0 Then VU =, diagonal. 0 2 So the columns of U are eigenvectors (corresponding to λ = 4, 2, resp.) of UV =
75 Classification Results Further refinements 3 2 ( ) Example. U = and V = ( ) 4 0 Then VU =, diagonal. 0 2 So the columns of U are eigenvectors (corresponding to λ = 4, 2, resp.) of UV = w = 1 has V w = 0. So it is an eigenvector for UV 1 corresponding to λ = 0.
76 Classification Results Further refinements
77 Classification Results Further refinements Recall: A = PDP 1 approach. Idea 1. Make sure P has determinant ±1. How to construct such P?
78 Classification Results Further refinements One technique [Ortega, 1984] uses: Take u, v R n. Then P = I n + u v has det P = 1 + u v.
79 Classification Results Further refinements One technique [Ortega, 1984] uses: Take u, v R n. Then P = I n + u v has det P = 1 + u v. So choose u v. Then P = I n + u v has det P = 1
80 Classification Results Further refinements One technique [Ortega, 1984] uses: Take u, v R n. Then P = I n + u v has det P = 1 + u v. So choose u v. Then P = I n + u v has det P = 1 and P 1 = I n u v.
81 Classification Results Further refinements One technique [Ortega, 1984] uses: Take u, v R n. Then P = I n + u v has det P = 1 + u v. So choose u v. Then P = I n + u v has det P = 1 and P 1 = I n u v. This is just a special case of what we have been doing with the Folk Theorem and the Shift Propostion.
82 Classification Results Further refinements 1 Example. Try u = 2 4
83 Classification Results Further refinements 1 2 Example. Try u = 2 and v = 1. (Check: u v = 0.) 4 1
84 Classification Results Further refinements 1 2 Example. Try u = 2 and v = 1. (Check: u v = 0.) 4 1 Then u v =
85 Classification Results Further refinements 1 2 Example. Try u = 2 and v = 1. (Check: u v = 0.) 4 1 Then u v = Get and P = I + u v = P 1 = I u v =
86 Classification Results Further refinements So Ortega uses this to say that any A = PDP 1 will be an IMIE.
87 Classification Results Further refinements So Ortega uses this to say that any A = PDP 1 will be an IMIE. But in fact, P itself is an IMIE. It has the single eigenvalue 1. P is clearly non-diagonalizable
88 Classification Results Further refinements Ortega s full result is that if u v = β then P = I n + u v has det P = 1 + β and (if β 1) then P 1 = I n 1 1+β u v. The case β = 2 is interesting.
89 Classification Results Further refinements Earlier we had UV = ( ) ( ) 2 (1 ) = with λ = 2, 0
90 Classification Results Further refinements Earlier we had UV = ( ) ( ) 2 (1 ) = with λ = 2, 0 So Q = UV + I = ( ) has eigenvalues λ = 1, 1. In fact, this Q is coming from Ortega s construction. So Q 1 = Q.
91 Classification Results Further refinements
92 Christopher Towse and Eric Campbell. Constructing integer matrices with integer eigenvalues. The Math. Scientist 41(2): 45 52, W. P. Galvin., The Teaching of Mathematics: Matrices with Custom-Built Eigenspaces. Amer. Math. Monthly, 91(5): , Greg Martin and Erick Wong. Almost all integer matrices have no integer eigenvalues.amer. Math. Monthly, 116(7): , James M. Ortega. Comment on: Matrices with integer entries and integer eigenvalues. Amer. Math. Monthly, 92(7): 526, J.-C. Renaud.Matrices with integer entries and integer eigenvalues. Amer. Math. Monthly, 90(3): , 1983.
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