Answer to Question #259092 in Linear Algebra for kofi

Question #259092

Consider the linear eigenproblem, 𝐴π‘₯=πœ†π‘₯, for the matrix

D=[1 1 2

2 1 1

1 1 3 ]

Solve for the largest eigenvalue by the direct method using the secant method. Let πœ†^((0))=5 and πœ†^((1))=4 Solve for the eigenvalues by the QR method


1
Expert's answer
2021-11-09T01:05:09-0500

Part one


"D= \\begin{pmatrix}\n 1&1 &2 \\\\\n 2&1 & 1\\\\\n1&1&3\n\\end{pmatrix}"


Characteristic equation "det||D-\\lambda\\Iota||=0"


"Det \\begin{pmatrix}\n 1-\\lambda&1&2 \\\\\n 2&1-\\lambda&1\\\\\n1&1&3-\\lambda\n\\end{pmatrix}"


Characteristic polynomial is of the form;


"\\lambda^3-A_1\\lambda^2+A_2\\lambda-A_3=0"

Where "A_1=" sum of main diagonal element

"=1+1+3=5"


"A_2=" sum of minors of the main diagonal element

"A_2= \\begin{vmatrix}\n 1 & 1 \\\\\n 1 & 3\n\\end{vmatrix}+\\begin{vmatrix}\n 1 & 2 \\\\\n 1 & 3\n\\end{vmatrix}+\\begin{vmatrix}\n 1& 1 \\\\\n 2 & 1\n\\end{vmatrix}=2+1-1=2"


"A_3= det(D)=\\quad\\quad\\quad\\quad1\\begin{vmatrix}\n 1 & 1\\\\\n 1 & 3\n\\end{vmatrix}-1\\begin{vmatrix}\n 2& 1\\\\\n 1 & 3\n\\end{vmatrix}+2\\begin{vmatrix}\n 2& 1\\\\\n 1& 1\n\\end{vmatrix}"


"=1(2)-1(5)+2(1)"

"=-1"


Characteristic polynomial is


"\\lambda^3-5\\lambda^2+2\\lambda+1=0"



Let "\\lambda^3-5\\lambda^2+2\\lambda+1=0"

"f(\\lambda)=\\lambda^3-5\\lambda^2+2\\lambda+1"



1st iteration


"\\lambda_0=5" and "\\lambda_1=4"

"f(\\lambda_0)=f(s)=11"

"f(\\lambda_1)=f(4)=-7"


"\\therefore\\>\\lambda_2=5-11" "\\frac{4-5}{-7-11}=4.3889"

"f(\\lambda_2)=-1.9937"


2nd iteration


"\\lambda_1=4" and "\\lambda_2=4.3889"

"f(\\lambda_1)=f(4)=-7"

"f(\\lambda_2)=f(4.3889)= -1.9937"


"\\lambda_3=4-(-7)" "\\frac{4.3889-4}{-1.9937-(-7)}=4.5438"


3rd iteration


"\\lambda_2=4.3889" and "\\lambda_3=4.5438"

"f(\\lambda_2)=-1.9937"

"f(\\lambda_3)=f(4.5438)=0.6688"

"\\lambda_4=4.3889-(-1.9937)"


"\\frac{4.5438-4.3889}{0.6688-\\>-1.9937}"

"=4.5049"

"f(\\lambda_4)=f(4.5049)=-0.0378"



4th iteration


"\\lambda_3=4.5438" and "\\lambda_4=4.5049"


"f(\\lambda_3)=0.6688"

"f(\\lambda_4)=-0.0378"


"\\lambda_5=4.5438-0.6688"

"\\frac{4.5049-4.5438}{-0.0378-0.6688}"

"=4.5070"


"f(\\lambda_5)=-0.0003"

Approximate root "=4.507"


Part two

"Q" -"R" method


Let "a_1= \\begin{bmatrix}\n 1&2&1 \n \n\\end{bmatrix}" "a_2=\\begin{bmatrix}\n 1&1& 1 \n \n\\end{bmatrix}"

and

"a_3=\\begin{bmatrix}\n 2&1 & 3 \n \n\\end{bmatrix}"


Let orthogonal set "=\\begin{pmatrix}\n b_1& b_2&b_3 \n \n\\end{pmatrix}"


Let orthonormal set "=\\begin{pmatrix}\n q_1&q_2& q_3\n \n\\end{pmatrix}"


Let "b_1= \\begin{bmatrix}\n 1&2 & 1 \n \n\\end{bmatrix},"


"||b_1||=\\sqrt{1^2+2^2+1^2}=2.45"


"q_1=\\frac{1}{2.45}\\begin{bmatrix}\n 1&2 & 1 \n \n\\end{bmatrix}=\\begin{bmatrix}\n \n\n0.41,&0.82,& 0.41\n \n\\end{bmatrix}"


"b_2=\\begin{bmatrix}\n 1&1& 1 \n \n\\end{bmatrix}-"

"<(1,1,1),(0.41,0.82,0.41)>(0.41,0.82,0.41)"


"=\\begin{pmatrix}\n 0.33,&-0.33,&0.33 \n \n\\end{pmatrix}"


"||b_2||= \\sqrt{ 0.33^2+0.33^2+0.33^2}="

"0.58"


"q_2= \\frac{1}{0.58}\\begin{bmatrix}\n 0.33,&-0.33& 0.33 \n \n\\end{bmatrix}="

"\\begin{pmatrix}\n \n 0.58,&-0.58,&0.58\n \n\\end{pmatrix}"


"b_3=\\begin{pmatrix}\n 2,&1,&3 \n \n\\end{pmatrix}" "-"

"-<(2,1,3),(0.41,0.82,0.41)>"

"(0.41,0.82,0.41)"


"- <(2,1,3),(0.58,-0.58,0.58)>"


"b_3=(-0.5,0,0.5)\\quad ||b_3||="


"\\sqrt{0.5^2+0^2+0.5^2}=0.71"


"q_3=\\frac{1}{0.71}(-0.5,0,0.5)="

"(-0.71,0,0.71)"


"\\therefore Q= \\begin{bmatrix}\n q_1&q_2& q_3\n \n\\end{bmatrix}="

"\\begin{bmatrix}\n 0.41&0.58 & -0.71\\\\\n 0.82&-0.58 & 0\\\\\n0.41&0.58&0.71\n\\end{bmatrix}"


"R=Q^TA=\\begin{pmatrix}\n 0.41&0.82 & 0.41\\\\\n 0.58&-0.58 & 0.58\\\\\n-0.71&0&0.71\n\\end{pmatrix}\\begin{pmatrix}\n 1&1 & 2\\\\\n 2&1& 1\\\\\n1&1&3\n\\end{pmatrix}"


"=\\begin{pmatrix}\n 2.45&1.63& 2.86\\\\\n 0&0.58&2.31\\\\\n0&0&0.71\n \n\\end{pmatrix}"


Denoting the above "R" as "R_0" and the "Q" as "Q_0"

Let "A_1=R_0Q_0"

Let "A_1=Q_1R_1" be "QR" factorization of "A_1" and similarly create "A_2=R_1Q_1"


This process is continued until "A_m" is created such that "A_m=Q_mR_m" and "A_{m1}=R_mQ_m" are equal.

At convergence the diagonal entries below the main diagonal are sufficiently small.


The diagonal entries of "A_m" will be eigenvalues of matrix "A"


The expected values will be approximate to "0.28514,0.77812 \\>and \\> 4 .50701"



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