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Determinants and Linear SystemCramer’s Rule
333231
232221
131211
33323
23222
13121
1
33323
23222
13121
3
2
1
333231
232221
131211
3
2
1
3
2
1
333231
232221
131211
det
det
10
01
00
aaa
aaa
aaa
aab
aab
aab
x
aab
aab
aab
x
x
x
aaa
aaa
aaa
b
b
b
x
x
x
aaa
aaa
aaa
Cramer’s Rule
• If det A is not zero, then Ax = b has the unique solution
ni
A
aabaax niii
,...,2,1
det
],,,,,det[ 111
Computer Science
• Graphs: G = (V,E)
View Internet Graph on Spheres
Adjacency Matrix:
01001
10100
01011
00101
10110
A
1
2
34
5
edgean not is j)(i, if 0
edgean is j)(i, if 1ijA
Matrix of GraphsAdjacency Matrix:• If A(i, j) = 1: edge exists
Else A(i, j) = 0.
0101
0000
1100
00101 2
34
1
-3
3
2 4
Matrix of Weighted GraphsWeighted Matrix:• If A(i, j) = w(i,j): edge exists
Else A(i, j) = infty.
032
0
430
101 2
34
1
-3
3
2 4
Random walks Transition Matrix
1
2
345
6
02
1
4
100
2
13
10
4
1000
3
1
2
10
2
1
3
10
004
10
3
10
004
1
2
10
2
13
1000
3
10
P
Markov Matrix
• Every entry is non-negative
• Every column adds to 1
• A Markov matrix defines a Markov chain
Term-Document Matrix• Index each document (by human or by
computer)– fij counts, frequencies, weights, etc
m term
2 term
1 term
n docdoc21 doc
21
22221
1 1211
mnmm
n
n
fff
fff
fff
• Each document can be regarded as a point in m dimensions
Document-Term Matrix• Index each document (by human or by
computer)– fij counts, frequencies, weights, etc
m doc
2 doc
1 doc
n term2 term1 term
21
22221
1 1211
mnmm
n
n
fff
fff
fff
• Each document can be regarded as a point in n dimensions
c1 c2 c3 c4 c5 m1 m2 m3 m4 human 1 0 0 1 0 0 0 0 0 interface 1 0 1 0 0 0 0 0 0 computer 1 1 0 0 0 0 0 0 0 user 0 1 1 0 1 0 0 0 0 system 0 1 1 2 0 0 0 0 0 response 0 1 0 0 1 0 0 0 0 time 0 1 0 0 1 0 0 0 0 EPS 0 0 1 1 0 0 0 0 0 survey 0 1 0 0 0 0 0 0 1 trees 0 0 0 0 0 1 1 1 0 graph 0 0 0 0 0 0 1 1 1 minors 0 0 0 0 0 0 0 1 1