Consider the linear eigenproblem, π΄π₯=ππ₯, 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^((0))=5 and π^((1))=4 Solve for the eigenvalues by the QR method
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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