Solution: Given that "T(x, y, z)=(2x+y-2z, 2x+3y-4z, x + y-z)"
First we find matrix representation of T relative to the standard basis B={(1,0,0),(0,1,0),(0,0,1)}
So matrix obtained is A = [ 2 1 − 2 2 3 − 4 1 1 − 1 ] A=\left[\begin{array}{ccc}2&1&-2\\2&3&-4\\1&1&-1\\\end{array}\right] A = ⎣ ⎡ 2 2 1 1 3 1 − 2 − 4 − 1 ⎦ ⎤
To find eigenvalues , we consider characteristic equation,
∣ 2 − λ 1 − 2 2 3 − λ − 4 1 1 − 1 − λ ∣ = 0 \left|\begin{array}{ccc}2-\lambda&1&-2\\2&3-\lambda&-4\\1&1&-1-\lambda\\\end{array}\right|=0 ∣ ∣ 2 − λ 2 1 1 3 − λ 1 − 2 − 4 − 1 − λ ∣ ∣ = 0
we know that characteristic equation is given by
λ 3 − ( T r a c e o f A ) λ 2 + ( S u m o f m i n o r s a l o n g d i a g o n a l ) λ − d e t ( A ) = 0 \lambda^3-(Trace~of~A )\lambda^2+(Sum ~of~minors~along~diagonal)\lambda-det(A)=0 λ 3 − ( T r a ce o f A ) λ 2 + ( S u m o f min ors a l o n g d ia g o na l ) λ − d e t ( A ) = 0
Trace of A=sum of diagonal elements=2+3-1=4
Sum of minors along diagonal=1+0+4=5
det(A)=2
∴ λ 3 − ( T r a c e o f A ) λ 2 + ( S u m o f m i n o r s a l o n g d i a g o n a l ) λ − d e t ( A ) = 0 \therefore\lambda^3-(Trace~of~A )\lambda^2+(Sum ~of~minors~along~diagonal)\lambda-det(A)=0 ∴ λ 3 − ( T r a ce o f A ) λ 2 + ( S u m o f min ors a l o n g d ia g o na l ) λ − d e t ( A ) = 0 is written as
λ 3 − ( 4 ) λ 2 + ( 5 ) λ − 2 = 0 \lambda^3-(4 )\lambda^2+(5)\lambda-2=0 λ 3 − ( 4 ) λ 2 + ( 5 ) λ − 2 = 0
λ 3 − 4 λ 2 + 5 λ − 2 = 0 \lambda^3-4 \lambda^2+5\lambda-2=0 λ 3 − 4 λ 2 + 5 λ − 2 = 0 is called the characteristic equation.
Put λ = 1 \lambda=1 λ = 1 the characteristic equation satisfies. Therefore \lambda=1 is one of the eigen value of the characteristic equation.
Now we find other eigen values by synthetic division method.
1|1 -4 5 -2
| 1 -3 2
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| 1 -3 2 0
λ 2 − 3 λ + 2 = 0 \lambda^2-3\lambda+2=0 λ 2 − 3 λ + 2 = 0
λ 2 − 2 λ − λ + 2 = 0 \lambda^2-2\lambda-\lambda+2=0 λ 2 − 2 λ − λ + 2 = 0
λ ( λ − 2 ) − 1 ( λ − 2 ) = 0 \lambda(\lambda-2)-1(\lambda-2)=0 λ ( λ − 2 ) − 1 ( λ − 2 ) = 0
( λ − 1 ) ( λ − 2 ) = 0 (\lambda-1)(\lambda-2)=0 ( λ − 1 ) ( λ − 2 ) = 0
λ = 1 , λ = 2 \lambda=1,\lambda=2 λ = 1 , λ = 2
Therefore the eigenvalues are λ = 1 , 1 , 2 \lambda=1,1,2 λ = 1 , 1 , 2
To find eigenspace corresponding to λ = 1 : \lambda=1: λ = 1 :
Put λ = 1 \lambda=1 λ = 1 in the equation ( A − λ I ) X = 0 w h e r e X i s v e c t o r X = [ x y z ] (A-\lambda I)X=0~ where ~X ~is vector ~X=\left[\begin{array}{ccc}x\\y\\z\\\end{array}\right] ( A − λ I ) X = 0 w h ere X i s v ec t or X = ⎣ ⎡ x y z ⎦ ⎤
∴ [ 2 − λ 1 − 2 2 3 − λ − 4 1 1 − 1 − λ ] [ x y z ] = [ 0 0 0 ] \therefore\left[\begin{array}{ccc}2-\lambda&1&-2\\2&3-\lambda&-4\\1&1&-1-\lambda\\\end{array}\right]\left[\begin{array}{ccc}x\\y\\z\\\end{array}\right] =\left[\begin{array}{ccc}0\\0\\0\\\end{array}\right] ∴ ⎣ ⎡ 2 − λ 2 1 1 3 − λ 1 − 2 − 4 − 1 − λ ⎦ ⎤ ⎣ ⎡ x y z ⎦ ⎤ = ⎣ ⎡ 0 0 0 ⎦ ⎤
∴ [ 1 1 − 2 2 2 − 4 1 1 − 2 ] [ x y z ] = [ 0 0 0 ] \therefore\left[\begin{array}{ccc}1&1&-2\\2&2&-4\\1&1&-2\\\end{array}\right]\left[\begin{array}{ccc}x\\y\\z\\\end{array}\right] =\left[\begin{array}{ccc}0\\0\\0\\\end{array}\right] ∴ ⎣ ⎡ 1 2 1 1 2 1 − 2 − 4 − 2 ⎦ ⎤ ⎣ ⎡ x y z ⎦ ⎤ = ⎣ ⎡ 0 0 0 ⎦ ⎤
Solving this by by Gauss elimination method, we get y a n d z y~and~z y an d z as free variable.
∴ X = [ x y z ] = [ − y + 2 z y z ] = y [ − 1 1 0 ] + z [ 2 0 1 ] = y [ v 1 ] + z [ v 2 ] \therefore X=\left[\begin{array}{ccc}x\\y\\z\\\end{array}\right]=\left[\begin{array}{ccc}-y+2z\\y\\z\\\end{array}\right]=y\left[\begin{array}{ccc}-1\\1\\0\\\end{array}\right] +z\left[\begin{array}{ccc}2\\0\\1\\\end{array}\right] =y[v1]+z[v2] ∴ X = ⎣ ⎡ x y z ⎦ ⎤ = ⎣ ⎡ − y + 2 z y z ⎦ ⎤ = y ⎣ ⎡ − 1 1 0 ⎦ ⎤ + z ⎣ ⎡ 2 0 1 ⎦ ⎤ = y [ v 1 ] + z [ v 2 ]
W h e r e v 1 = [ − 1 1 0 ] a n d v 2 = [ 2 0 1 ] Where~v1=\left[\begin{array}{ccc}-1\\1\\0\\\end{array}\right] ~and~v2=\left[\begin{array}{ccc}2\\0\\1\\\end{array}\right] Wh ere v 1 = ⎣ ⎡ − 1 1 0 ⎦ ⎤ an d v 2 = ⎣ ⎡ 2 0 1 ⎦ ⎤ a r e t w o e i g e n v e c t o r s c o r r e s p o n d i n g t o λ = 1 ~are~two~eigen~vectors~corresponding~ to~\lambda=1 a re tw o e i g e n v ec t ors corres p o n d in g t o λ = 1
To find eigenspace corresponding to λ = 2 : \lambda=2: λ = 2 :
Put λ = 2 \lambda=2 λ = 2 in the equation ( A − λ I ) X = 0 w h e r e X i s v e c t o r X = [ x y z ] (A-\lambda I)X=0~ where ~X ~is vector ~X=\left[\begin{array}{ccc}x\\y\\z\\\end{array}\right] ( A − λ I ) X = 0 w h ere X i s v ec t or X = ⎣ ⎡ x y z ⎦ ⎤
∴ [ 2 − λ 1 − 2 2 3 − λ − 4 1 1 − 1 − λ ] [ x y z ] = [ 0 0 0 ] \therefore\left[\begin{array}{ccc}2-\lambda&1&-2\\2&3-\lambda&-4\\1&1&-1-\lambda\\\end{array}\right]\left[\begin{array}{ccc}x\\y\\z\\\end{array}\right] =\left[\begin{array}{ccc}0\\0\\0\\\end{array}\right] ∴ ⎣ ⎡ 2 − λ 2 1 1 3 − λ 1 − 2 − 4 − 1 − λ ⎦ ⎤ ⎣ ⎡ x y z ⎦ ⎤ = ⎣ ⎡ 0 0 0 ⎦ ⎤
∴ [ 0 1 − 2 2 1 − 4 1 1 − 3 ] [ x y z ] = [ 0 0 0 ] \therefore\left[\begin{array}{ccc}0&1&-2\\2&1&-4\\1&1&-3\\\end{array}\right]\left[\begin{array}{ccc}x\\y\\z\\\end{array}\right] =\left[\begin{array}{ccc}0\\0\\0\\\end{array}\right] ∴ ⎣ ⎡ 0 2 1 1 1 1 − 2 − 4 − 3 ⎦ ⎤ ⎣ ⎡ x y z ⎦ ⎤ = ⎣ ⎡ 0 0 0 ⎦ ⎤
Solving this by by Gauss elimination method, we get z z z as free variable.
∴ X = [ x y z ] = [ z 2 z z ] = z [ 1 2 1 ] = z [ v 3 ] \therefore X=\left[\begin{array}{ccc}x\\y\\z\\\end{array}\right]=\left[\begin{array}{ccc}z\\2z\\z\\\end{array}\right]=z\left[\begin{array}{ccc}1\\2\\1\\\end{array}\right] =z[v3] ∴ X = ⎣ ⎡ x y z ⎦ ⎤ = ⎣ ⎡ z 2 z z ⎦ ⎤ = z ⎣ ⎡ 1 2 1 ⎦ ⎤ = z [ v 3 ]
W h e r e v 3 = [ 1 2 1 ] i s t h e e i g e n v e c t o r s c o r r e s p o n d i n g t o λ = 2 Where~v3=\left[\begin{array}{ccc}1\\2\\1\\\end{array}\right] ~is~the~eigen~vectors~corresponding~ to~\lambda=2 Wh ere v 3 = ⎣ ⎡ 1 2 1 ⎦ ⎤ i s t h e e i g e n v ec t ors corres p o n d in g t o λ = 2 .
Now, to check T is diagonalizable :
To check T is diagonalizable or not, it is sufficient to prove that A P = P D AP=PD A P = P D
w h e r e A = [ 2 1 − 2 2 3 − 4 1 1 − 1 ] , P = [ v 1 v 2 v 3 ] = [ − 1 2 1 1 0 2 0 1 1 ] where~A=\left[\begin{array}{ccc}2&1&-2\\2&3&-4\\1&1&-1\\\end{array}\right],~P=[v1~v2~v3]=\left[\begin{array}{ccc}-1&2&1\\1&0&2\\0&1&1\\\end{array}\right] w h ere A = ⎣ ⎡ 2 2 1 1 3 1 − 2 − 4 − 1 ⎦ ⎤ , P = [ v 1 v 2 v 3 ] = ⎣ ⎡ − 1 1 0 2 0 1 1 2 1 ⎦ ⎤
a n d D = [ 1 0 0 0 1 0 0 0 2 ] = d i a g o n a l e l e m e n t s o f e i g e n v a l u e s . ~and~D=\left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&2\\\end{array}\right]=diagonal ~elements~ of~eigen~values. an d D = ⎣ ⎡ 1 0 0 0 1 0 0 0 2 ⎦ ⎤ = d ia g o na l e l e m e n t s o f e i g e n v a l u es .
Now, A P = [ 2 1 − 2 2 3 − 4 1 1 − 1 ] [ − 1 2 1 1 0 2 0 1 1 ] [ m a t r i x m u l t i p l i c a t i o n ] AP=\left[\begin{array}{ccc}2&1&-2\\2&3&-4\\1&1&-1\\\end{array}\right]\left[\begin{array}{ccc}-1&2&1\\1&0&2\\0&1&1\\\end{array}\right] ~~~~~~~~~~~~~~~~~[matrix~ multiplication] A P = ⎣ ⎡ 2 2 1 1 3 1 − 2 − 4 − 1 ⎦ ⎤ ⎣ ⎡ − 1 1 0 2 0 1 1 2 1 ⎦ ⎤ [ ma t r i x m u lt i pl i c a t i o n ]
= [ − 1 2 2 1 0 4 0 1 2 ] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ( 1 ) =\left[\begin{array}{ccc}-1&2&2\\1&0&4\\0&1&2\\\end{array}\right]..................................(1) = ⎣ ⎡ − 1 1 0 2 0 1 2 4 2 ⎦ ⎤ .................................. ( 1 )
P D = [ − 1 2 1 1 0 2 0 1 1 ] [ 1 0 0 0 1 0 0 0 2 ] PD=\left[\begin{array}{ccc}-1&2&1\\1&0&2\\0&1&1\\\end{array}\right]\left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&2\\\end{array}\right] P D = ⎣ ⎡ − 1 1 0 2 0 1 1 2 1 ⎦ ⎤ ⎣ ⎡ 1 0 0 0 1 0 0 0 2 ⎦ ⎤
= [ − 1 2 2 1 0 4 0 1 2 ] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ( 2 ) =\left[\begin{array}{ccc}-1&2&2\\1&0&4\\0&1&2\\\end{array}\right]..................................(2) = ⎣ ⎡ − 1 1 0 2 0 1 2 4 2 ⎦ ⎤ .................................. ( 2 )
f r o m ( 1 ) a n d ( 2 ) , A P = P D from~(1)~and~(2)~,AP=PD f ro m ( 1 ) an d ( 2 ) , A P = P D
Hence T is diagonalizable.
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