linear algebra
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Linear algebra. Motivating example: Web start-up. Eigenvector-eigenvalue analysis. Vector space and basis. Linear operators and representations. +. Example: Modeling a freemium cloud data storage business. Free. Premium. +. +. +. - PowerPoint PPT PresentationTRANSCRIPT
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Linear algebra
1
Linear operators and representations
Motivating example: Web start-up
Vector space and basis
Eigenvector-eigenvalue analysis
+
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ
๐ฃ
[๐ฃ1โฒ๐ฃ2โฒ ]=[๐ธ 1 ,1 ๐ธ 1 ,2
๐ธ 2 ,1 ๐ธ 2 ,2] [๐ฃ1๐ฃ2]
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ=๐๐ฃ
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๐ฅ๐น ๐ฅ๐
๐ฅ๐น (๐ก+โ ๐ก )=๐ฅ๐น (๐ก )+๐๐ฅ ๐ (๐ก )โ๐๐ฅ๐น (๐ก )+๐ฟ๐ฅ๐ (๐ก )โ๐ผ๐ฅ๐น (๐ก )
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๐ฅ๐ (๐ก+โ ๐ก )=๐ฅ๐ (๐ก )+๐ ๐ฅ๐ (๐ก )+๐ ๐ฅ๐น (๐ก )โ๐ฟ๐ฅ ๐ (๐ก )โ๐ผ ๐ฅ๐น (๐ก )
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Linear algebra
7
Linear operators and representations
Motivating example: Web start-up
Vector space and basis
Eigenvector-eigenvalue analysis
+
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ
๐ฃ
[๐ฃ1โฒ๐ฃ2โฒ ]=[๐ธ 1 ,1 ๐ธ 1 ,2
๐ธ 2 ,1 ๐ธ 2 ,2] [๐ฃ1๐ฃ2]
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ=๐๐ฃ
![Page 8: Linear algebra](https://reader035.vdocuments.pub/reader035/viewer/2022062323/56815ac0550346895dc8845b/html5/thumbnails/8.jpg)
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Vector
๐ฃ
A vector is an arrow. The position of the head in relation to the tail is expressed in terms of a magnitude and direction.
![Page 9: Linear algebra](https://reader035.vdocuments.pub/reader035/viewer/2022062323/56815ac0550346895dc8845b/html5/thumbnails/9.jpg)
9
A set of vectors
๐ฃ
๏ฟฝโ๏ฟฝ๏ฟฝโ๏ฟฝ
๐ฆ๏ฟฝโ๏ฟฝ
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A set of vectors
๐ฃ
๏ฟฝโ๏ฟฝ๏ฟฝโ๏ฟฝ
๐ฆ๏ฟฝโ๏ฟฝ
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๐ฃ
๏ฟฝโ๏ฟฝ๏ฟฝโ๏ฟฝ
๐ฆ
๐ฃ+๐ค
๏ฟฝโ๏ฟฝ
โHead-to-tailโ addition of and produced a resultant vector not belonging to our original set of vectors
A set of vectors vs. a vector spaceThis scaling (doubling length in this example) of produced 2, which belongs to our original set of vectors
A vector space is a set of vectors that is โclosedโ under scaling and vector addition. Neither scaling nor vector addition produces a result not already included in the โspace.โ
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BasisA vector space is a set of vectors that are โclosedโ under scaling and vector addition.
A set of vectors , , . . .
Linear combination: addition of vectors with scalings
Used a set of vectors to prescribe a vector space!
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Basis set: Canโt remove any vector without changing space
A vector space is a set of vectors that are โclosedโ under scaling and vector addition.
A set of vectors , , . . .
Linear combination: addition of vectors with scalings
Basis for V vector space V2-dimensionalN
S
EW
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Basis: Coordinate system
Linear combination: addition of vectors with scalings
A vector space is a set of vectors that are โclosedโ under scaling and vector addition.
A set of vectors , , . . .
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15
Basis: Coordinate system
Linear combination: addition of vectors with scalings
A vector space is a set of vectors that are โclosedโ under scaling and vector addition.
A set of vectors , , . . .
๐ฃ=๐ฃ1๐1+๐ฃ2๐2
๐1 ๐2
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16
Basis: Coordinate system
Linear combination: addition of vectors with scalings
A vector space is a set of vectors that are โclosedโ under scaling and vector addition.
A set of vectors , , . . .
๐ฃ=๐ฃ1๐1+๐ฃ2๐2
๐1 ๐2
๏ฟฝโ๏ฟฝ1๏ฟฝโ๏ฟฝ2 ๐ฃ=๐ฃ1 ๏ฟฝโ๏ฟฝ1+๐ฃ2 ๏ฟฝโ๏ฟฝ2
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Linear algebra
17
Linear operators and representations
Motivating example: Web start-up
Vector space and basis
Eigenvector-eigenvalue analysis
+
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ
๐ฃ
[๐ฃ1โฒ๐ฃ2โฒ ]=[๐ธ 1 ,1 ๐ธ 1 ,2
๐ธ 2 ,1 ๐ธ 2 ,2] [๐ฃ1๐ฃ2]
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ=๐๐ฃ
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Operator
๐ฃ
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝGiven a vector, an operator outputs a vector, possibly scaled and/or rotated
A function associates objects from a domain with objects in a codomain, sometimes in terms of elementary arithmetic operations.
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19
Linear operators
๐ผ๐ฃ
๏ฟฝฬ๏ฟฝ๐ผ ๏ฟฝโ๏ฟฝ=๐ผ ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ
Scaling Addition
๐ฃ ๏ฟฝโ๏ฟฝ๐ฃ+๐ค
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ
๐ฃ
๏ฟฝฬ๏ฟฝ๐ค๏ฟฝโ๏ฟฝ
๏ฟฝฬ๏ฟฝ (๐ผ๏ฟฝโ๏ฟฝ+๐ฝ๐ค )=๐ผ ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ+๐ฝ ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ
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Representing linear operators
๏ฟฝฬ๏ฟฝ (๐ผ๏ฟฝโ๏ฟฝ+๐ฝ๐ค )=๐ผ ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ+๐ฝ ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ
๐ฃ=๐ฃ1๐1+๐ฃ2๐2
๐1 ๐2
ยฟ๐ฃ1 ๏ฟฝฬ๏ฟฝ๐1+๐ฃ2 ๏ฟฝฬ๏ฟฝ๐2๐ฃ โฒ= ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ
๐ฃ โฒ=๐ฃ1โฒ ๐1+๐ฃ2
โฒ ๐2
ยฟ๐ฃ1 [( ๏ฟฝฬ๏ฟฝ๐1)1๐1+ ( ๏ฟฝฬ๏ฟฝ ๐1 )2๐2 ]ยฟ ๏ฟฝฬ๏ฟฝ (๐ฃ1๐1+๐ฃ2๐2 )
+๐ฃ2 [( ๏ฟฝฬ๏ฟฝ ๐2 )1๐1+( ๏ฟฝฬ๏ฟฝ๐2 )2๐2 ]ยฟ๐ฃ1 [๐ธ1 , 1๐1+๐ธ 2 ,1๐2 ]
+๐ฃ2 [๐ธ 1, 2๐1+๐ธ2 , 2๐2 ]
ยฟ (๐ฃ1๐ธ1 , 1+๐ฃ2๐ธ 1, 2 )๐1+(๐ฃ1๐ธ 2, 1+๐ฃ2๐ธ 2 ,2 )๐2
๐ฃ1โฒ ๐1+๐ฃ2โฒ ๐2
๐ฃ1โฒ=๐ธ 1 ,1๐ฃ1+๐ธ 1 ,2๐ฃ2๐ฃ2โฒ=๐ธ2 ,1๐ฃ1+๐ธ2 , 2๐ฃ2
[๐ฃ1โฒ๐ฃ2โฒ ]=[๐ธ 1 ,1 ๐ธ 1 ,2
๐ธ 2 ,1 ๐ธ 2 ,2] [๐ฃ1๐ฃ2]
๐ธ1 , 2
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Representing linear operators
๐ฃ=๐ฃ1๐1+๐ฃ2๐2
๐1 ๐2
๐ฃ โฒ=๐ฃ1โฒ ๐1+๐ฃ2
โฒ ๐2
๐ฃ1โฒ=๐ธ 1 ,1๐ฃ1+๐ธ 1 ,2๐ฃ2๐ฃ2โฒ=๐ธ2 ,1๐ฃ1+๐ธ2 , 2๐ฃ2
[๐ฃ1โฒ๐ฃ2โฒ ]=[๐ธ 1 ,1 ๐ธ 1 ,2
๐ธ 2 ,1 ๐ธ 2 ,2] [๐ฃ1๐ฃ2]
๏ฟฝฬ๏ฟฝ (๐ผ๏ฟฝโ๏ฟฝ+๐ฝ๐ค )=๐ผ ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ+๐ฝ ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ ๐ฃ โฒ= ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ Abstract action on vector
Relationship between coefficients
Representation in the context of a particular basis
๐ฃ โฒโ [๐ฃ1โฒ๐ฃ2โฒ ] ๐ฃโ[๐ฃ1๐ฃ2]
๏ฟฝฬ๏ฟฝโ [๐ธ 1 ,1 ๐ธ 1 ,2
๐ธ 2 ,1 ๐ธ 2 ,2]
โThe vector v-prime is represented by the column vector v-prime-sub-1, v-prime-sub-2โ
โThe operator A is represented by the matrix Aโ
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Vector transformation algorithm implies matrix multiplication
๐ฃ โฒ= ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ
๐ฃ โฒ โฒ=๏ฟฝฬ๏ฟฝ ๐ฃ โฒ
๐ฃ
๏ฟฝฬ๏ฟฝ ๐ฃ โฒโ[๐น1 ,1 ๐น1 , 2
๐น2 ,1 ๐น2 , 2][ ๐ธ 1 ,1๐ฃ1+๐ธ 1 ,2๐ฃ2๐ธ 2, 1๐ฃ1+๐ธ 2 ,2๐ฃ2 ]
๐ฃโ[๐ฃ1๐ฃ2]๏ฟฝฬ๏ฟฝโ [๐ธ 1 ,1 ๐ธ 1 ,2
๐ธ 2 ,1 ๐ธ 2 ,2]๐ฃ1โฒ=๐ธ 1 ,1๐ฃ1+๐ธ 1 ,2๐ฃ2๐ฃ2โฒ=๐ธ2 ,1๐ฃ1+๐ธ2 , 2๐ฃ2
ยฟ [๐น1, 1 (๐ธ 1 ,1๐ฃ1+๐ธ1 ,2๐ฃ2 )+๐น1 ,2 (๐ธ 2 ,1๐ฃ1+๐ธ 2 ,2๐ฃ2 )๐น2, 1 (๐ธ 1 ,1๐ฃ1+๐ธ1 ,2๐ฃ2 )+๐น2 , 2 (๐ธ 2 ,1๐ฃ1+๐ธ 2 ,2๐ฃ2 )]
ยฟ [ (๐น1 ,1๐ธ1 ,1+๐น1 ,2๐ธ2 , 1 )๐ฃ1+ (๐น1 , 1๐ธ 1, 2+๐น1, 2๐ธ 2 ,2 )๐ฃ2(๐น2 ,1๐ธ 1 ,1+๐น2 ,2๐ธ2 , 1 )๐ฃ1+ (๐น2 ,1๐ธ 1 ,2+๐น2 ,2๐ธ2 , 2 )๐ฃ2]
ยฟ [๐น1, 1๐ธ 1, 1+๐น1, 2๐ธ 2 ,1 ๐น1 , 1๐ธ1 , 2+๐น1 , 2๐ธ 2 ,2
๐น2, 1๐ธ 1, 1+๐น2 ,2๐ธ 2 ,1 ๐น2 , 1๐ธ1 , 2+๐น2, 2๐ธ 2 ,2][๐ฃ1๐ฃ2]๏ฟฝฬ๏ฟฝ ๏ฟฝฬ๏ฟฝ๐ฃโ[๐น1 ,1 ๐น1 ,2
๐น2 ,1 ๐น2 ,2] [๐ธ1 ,1 ๐ธ1 , 2
๐ธ2 , 1 ๐ธ2 , 2] [๐ฃ1๐ฃ2]
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Linear algebra
23
Linear operators and representations
Motivating example: Web start-up
Vector space and basis
Eigenvector-eigenvalue analysis
+
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ
๐ฃ
[๐ฃ1โฒ๐ฃ2โฒ ]=[๐ธ 1 ,1 ๐ธ 1 ,2
๐ธ 2 ,1 ๐ธ 2 ,2] [๐ฃ1๐ฃ2]
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ=๐๐ฃ
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๐ฅ๐น ๐ฅ๐
๐ฅ๐น (๐ก+โ ๐ก )=๐ฅ๐น (๐ก )+๐๐ฅ ๐ (๐ก )โ๐๐ฅ๐น (๐ก )+๐ฟ๐ฅ๐ (๐ก )โ๐ผ๐ฅ๐น (๐ก )
Event โCausalโ subpopulation Fraction thereof
Recruit Premium users +1 0
Upgrade Free users -1 +1
Downgrade Premium users +1 -1
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๐ฅ๐ (๐ก+โ ๐ก )=๐ฅ๐ (๐ก )+๐ ๐ฅ๐ (๐ก )+๐ ๐ฅ๐น (๐ก )โ๐ฟ๐ฅ ๐ (๐ก )โ๐ผ ๐ฅ๐น (๐ก )
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Free Premium๐ฅ๐น ๐ฅ๐๐ฅ๐น (๐ก+โ ๐ก )=๐ฅ๐น (๐ก )+๐๐ฅ ๐ (๐ก )โ๐๐ฅ๐น (๐ก )+๐ฟ๐ฅ๐ (๐ก )โ๐ผ๐ฅ๐น (๐ก )๐ฅ๐ (๐ก+โ ๐ก )=๐ฅ๐ (๐ก )+๐ ๐ฅ๐ (๐ก )+๐ ๐ฅ๐น (๐ก )โ๐ฟ๐ฅ ๐ (๐ก )โ๐ผ ๐ฅ๐น (๐ก )
[๐ฅ๐น (๐ก+โ ๐ก )๐ฅ๐ (๐ก+โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ][๐ฅ๐น (๐ก )๐ฅ๐ (๐ก ) ]
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๏ฟฝโ๏ฟฝ (๐ก )=๐ฅ๐น (๐ก ) ๏ฟฝโ๏ฟฝ +๐ฅ๐ (๐ก ) ๏ฟฝโ๏ฟฝ
![Page 30: Linear algebra](https://reader035.vdocuments.pub/reader035/viewer/2022062323/56815ac0550346895dc8845b/html5/thumbnails/30.jpg)
0 0.25 0.5 0.75 1 1.25 1.5 1.750
0.25
0.5
0.75
1
1.25
1.5
1.75 ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ=๐๐ฃ
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๐ฅ ๐
๐
๐ฅ ๐น
๐
Easy-
lookin
g-one-d
imen
siona
l pro
blem
+
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ=๐๐ฃ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ=๐ ๐ผ ๏ฟฝโ๏ฟฝ
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝโ ๐ ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ= 0โ( ๏ฟฝฬ๏ฟฝโ ๐๐ผ ) ๏ฟฝโ๏ฟฝ= 0โ
([๐ธ1 ,1 ๐ธ1 , 2
๐ธ2 , 1 ๐ธ2 , 2]โ ๐ [1 00 1])[๐ฃ๐น
๐ฃ๐ ]=[00 ]([๐ ๐๐ ๐]โ ๐[1 0
0 1 ])[๐ฃ ๐น
๐ฃ ๐ ]=[00]
STOP
Check that
is consistent in a matrix representation
[๐โ ๐ ๐๐ ๐โ ๐][๐ฃ๐น
๐ฃ ๐ ]=[00](๐โ ๐ )๐ฃ ๐น+๐๐ฃ๐=0๐ ๐ฃ๐น+(๐โ๐ )๐ฃ ๐=0
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ=๐๐ฃ(๐โ ๐ ) (๐โ ๐ )๐ฃ๐น+ (๐โ ๐ )๐๐ฃ๐=0
๐๐ ๐ฃ๐น+๐ (๐โ ๐ )๐ฃ ๐=0- [ ][ (๐โ๐ ) (๐โ ๐ )โ๐๐ ]๐ฃ๐น=0
(๐โ ๐ ) (๐โ ๐ )โ๐๐=0๐๐โ ๐๐โ ๐๐+๐2โ๐๐=0
๐2โ (๐+๐ ) ๐+(๐๐โ๐๐ )=0
๐ยฑ=(๐+๐ )ยฑโ (๐+๐ )2โ4 (1 ) (๐๐โ๐๐ )
2 (1 )
๐ยฑ=(๐+๐ )ยฑโ๐2+2๐๐+๐2โ4 ๐๐+4๐๐
2
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ=๐๐ฃ๐ยฑ=
(๐+๐ )ยฑโ๐2+2๐๐+๐2โ4 ๐๐+4๐๐2
๐ยฑ=(๐+๐ )ยฑโ (๐โ๐ )2+4๐๐
2
๐ยฑ=(2โ๐โ๐ผโ๐ฟ )ยฑโ (๐ฟโ๐โ๐ผ )2+4 (๐+๐ฟ )๐
2There are 2 possibly special scaling factors. Does each l actually correspond to a special ?
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ=๐๐ฃ๐ยฑ=
(๐+๐ )ยฑโ (๐โ๐ )2+4๐๐2
There are 2 possibly special scaling factors. Does each l actually correspond to a special ?
[๐โ ๐ยฑ ๐๐ ๐โ ๐ยฑ] [๐ฃ ๐น
ยฑ
๐ฃ ๐ยฑ ]=[00]
(๐โ ๐ยฑ) ๐ฃ๐นยฑ +๐๐ฃ๐
ยฑ=0๐๐ฃ ๐
ยฑ= (๐ยฑโ๐ )๐ฃ๐นยฑ
๐ฃ ๐ยฑ=
๐ยฑโ๐๐ ๐ฃ ๐น
ยฑ
๐ฃ ๐ยฑ=
๐ผ+๐โ ๐ฟยฑโ (๐ฟโ๐โ๐ผ )2+4 (๐+๐ฟ )๐2 (๐+๐ฟ )
๐ฃ๐นยฑ
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ=๐๐ฃ๐ยฑ=
(๐+๐ )ยฑโ (๐โ๐ )2+4๐๐2
๐ฃ ๐ยฑ=
๐ผ+๐โ ๐ฟยฑโ (๐ฟโ๐โ๐ผ )2+4 (๐+๐ฟ )๐2 (๐+๐ฟ )
๐ฃ๐นยฑ
๐ยฑ
There are 2 special scaling factors; each l corresponds to a special vector . Unless something is hokey, they point in different directions and can serve as a basis.
๏ฟฝโ๏ฟฝ+ยฟ ยฟ ๐ฃโ
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๏ฟฝฬ๏ฟฝ ๐ฃยฑ=๐ยฑ ๐ฃยฑ๐ฃ ๐
ยฑ=๐ยฑ๐ฃ๐นยฑ
๏ฟฝโ๏ฟฝ (๐ก )=๐ฅ๐น (๐ก ) ๏ฟฝโ๏ฟฝ +๐ฅ๐ (๐ก ) ๏ฟฝโ๏ฟฝ๐ ๏ฟฝโ๏ฟฝ +0 ๏ฟฝโ๏ฟฝ=๐๐
+ ยฟโ๐ฃ+ยฟ+๐๐โ๐ฃโยฟ ยฟ
๏ฟฝโ๏ฟฝ+ยฟ ยฟ ๐ฃโ
๏ฟฝโ๏ฟฝ=๐+ยฟ ยฟยฟ
Inaugural trials
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๏ฟฝโ๏ฟฝ=๐+ยฟ ยฟยฟ
๏ฟฝโ๏ฟฝ=ยฟ๐+ยฟ+๐โ=1ยฟ ๐
+ยฟ๐ +ยฟ+๐ โ๐โ=0ยฟ ยฟ
๏ฟฝฬ๏ฟฝ ๐ฃยฑ=๐ยฑ ๐ฃยฑ๐ฃ ๐
ยฑ=๐ยฑ๐ฃ๐นยฑ
๏ฟฝโ๏ฟฝ (๐ก )=๐ฅ๐น (๐ก ) ๏ฟฝโ๏ฟฝ +๐ฅ๐ (๐ก ) ๏ฟฝโ๏ฟฝ๐ ๏ฟฝโ๏ฟฝ +0 ๏ฟฝโ๏ฟฝ=๐๐
+ ยฟโ๐ฃ+ยฟ+๐๐โ๐ฃโยฟ ยฟ
Inaugural trials
๏ฟฝโ๏ฟฝ=๐+ยฟ ยฟยฟ
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๐+ยฟ+๐โ=1ยฟ ๐+ยฟ๐ +ยฟ+๐ โ๐โ=0ยฟ ยฟ
๐+ยฟ๐ +ยฟ=โ ๐โ๐โยฟ ยฟ
๐+ยฟ=โ๐โ
๐โ
๐+ยฟยฟยฟโ๐โ
๐โ
๐+ยฟ+๐โ=1ยฟ
๐โยฟ๐โยฟ ๐โ=
๐+ยฟ
๐+ยฟโ๐โ
ยฟยฟ ๐
+ยฟ=โ ๐โ
๐+ยฟโ๐โ
ยฟยฟ
๏ฟฝโ๏ฟฝ (๐ก )=๐๐+ยฟโ ๐ฃ+ยฟ +๐ ๐โ๐ฃโยฟ ยฟ
๏ฟฝฬ๏ฟฝ ๐ฃยฑ=๐ยฑ ๐ฃยฑ๐ฃ ๐
ยฑ=๐ยฑ๐ฃ๐นยฑ
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๐โ=๐+ยฟ
๐+ยฟโ๐โ
ยฟยฟ ๐
+ยฟ=โ ๐โ
๐+ยฟโ๐โ
ยฟยฟ
๏ฟฝโ๏ฟฝ (๐ก )=๐๐+ยฟโ ๐ฃ+ยฟ +๐ ๐โ๐ฃโยฟ ยฟ
๏ฟฝฬ๏ฟฝ ๐ฃยฑ=๐ยฑ ๐ฃยฑ๐ฃ ๐
ยฑ=๐ยฑ๐ฃ๐นยฑ
๏ฟฝฬ๏ฟฝ ๏ฟฝฬ๏ฟฝ ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝ (๐ก )=โ ๐๐โ
๐+ยฟโ๐โ
๐+ยฟ๐+ยฟ ๐+ ยฟ ๏ฟฝฬ๏ฟฝโ
๐ฃ +ยฟ+ ๐๐+ยฟ
๐+ ยฟโ๐โ ๐โ ๐โ ๐โ ๏ฟฝฬ๏ฟฝ ๏ฟฝโ๏ฟฝโยฟยฟ ยฟ ยฟ
ยฟ ยฟยฟ
๏ฟฝฬ๏ฟฝ๐ ๏ฟฝโ๏ฟฝ (๐ก )=โ ๐๐โ
๐+ยฟโ๐โ
๐+ยฟ๐ ๐ฃ+ยฟ + ๐๐+ยฟ
๐+ยฟโ๐โ
๐โ๐๐ฃโยฟ
ยฟยฟ ยฟยฟ
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๏ฟฝฬ๏ฟฝ ๐ฃยฑ=๐ยฑ ๐ฃยฑ๐ฃ ๐
ยฑ=๐ยฑ๐ฃ๐นยฑ
๏ฟฝฬ๏ฟฝ๐ ๏ฟฝโ๏ฟฝ (๐ก )=โ ๐๐โ
๐+ยฟโ๐โ
๐+ยฟ๐ ๐ฃ+ยฟ + ๐๐+ยฟ
๐+ยฟโ๐โ
๐โ๐๐ฃโยฟ
ยฟยฟ ยฟยฟ
๏ฟฝโ๏ฟฝ (๐ก+๐โ ๐ก )=โ ๐๐โ
๐+ยฟโ๐โ
๐+ยฟ๐ยฟ ยฟยฟ
๐ฅ๐น (๐ก+๐โ ๐ก )=๐๐+ยฟ๐โ
๐โ๐โ ๐+ยฟ ๐
๐ +ยฟ โ๐โ
ยฟยฟ
ยฟ
๐ฅ๐ (๐ก+๐ โ ๐ก )=๐ ๐+ยฟ๐โ
๐+ยฟโ๐โ
ยฟยฟยฟ
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๏ฟฝฬ๏ฟฝ ๐ฃยฑ=๐ยฑ ๐ฃยฑ๐ฃ ๐
ยฑ=๐ยฑ๐ฃ๐นยฑ
๐ฅ๐น (๐ก+๐โ ๐ก )=๐๐+ยฟ๐โ
๐โ๐โ ๐+ยฟ ๐
๐ +ยฟ โ๐โ
ยฟยฟ
ยฟ
๐ฅ๐ (๐ก+๐ โ ๐ก )=๐ ๐+ยฟ๐โ
๐+ยฟโ๐โ
ยฟยฟยฟ
๐ยฑ=(2โ๐โ๐ผโ๐ฟ )ยฑโ (๐ฟโ๐โ๐ผ )2+4 (๐+๐ฟ )๐
2
๐ยฑ=๐ผ+๐โ๐ฟยฑโ (๐ฟโ๐โ๐ผ )2+4 (๐+๐ฟ )๐
2 (๐+๐ฟ )
, , , = 0.2, 0.2, 0.1, 0.1
![Page 42: Linear algebra](https://reader035.vdocuments.pub/reader035/viewer/2022062323/56815ac0550346895dc8845b/html5/thumbnails/42.jpg)
0 0.25 0.5 0.75 1 1.25 1.5 1.750
0.25
0.5
0.75
1
1.25
1.5
1.75
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๐ฅ ๐
๐
๐ฅ ๐น
๐
๏ฟฝฬ๏ฟฝ ๐ฃยฑ=๐ยฑ ๐ฃยฑ
, , , = 0.2, 0.2, 0.1, 0.1
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๐ฅ๐น
๐ฅ๐
[๐ฅ๐น (๐ก+๐โ ๐ก )๐ฅ๐ (๐ก+๐ โ ๐ก )]=[1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ][1โ๐โ๐ผ ๐+๐ฟ๐ 1โ๐ฟ]โฏ [1โ๐โ๐ผ ๐+๐ฟ
๐ 1โ๐ฟ ] [๐ฅ ๐น (๐ก )๐ฅ ๐ (๐ก )]
M copies of matrix
๏ฟฝฬ๏ฟฝ ๐ฃยฑ=๐ยฑ ๐ฃยฑ๐ฃ ๐
ยฑ=๐ยฑ๐ฃ๐นยฑ
๐ฅ๐น (๐ก+๐โ ๐ก )=๐๐+ยฟ๐โ
๐โ๐โ ๐+ยฟ ๐
๐ +ยฟ โ๐โ
ยฟยฟ
ยฟ
๐ฅ๐ (๐ก+๐ โ ๐ก )=๐ ๐+ยฟ๐โ
๐+ยฟโ๐โ
ยฟยฟยฟ
๐ยฑ=(2โ๐โ๐ผโ๐ฟ )ยฑโ (๐ฟโ๐โ๐ผ )2+4 (๐+๐ฟ )๐
2
๐ยฑ=๐ผ+๐โ๐ฟยฑโ (๐ฟโ๐โ๐ผ )2+4 (๐+๐ฟ )๐
2 (๐+๐ฟ )
Eigenvectors
Eigenvalues
, , , = 0.2, 0.2, 0.1, 0.1