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cds-numerical-methods/Week 4/8 Eigenvalues and Eigenvectors.ipynb
Koen Vendrig 611e8676bf Add week 4 including feedback
2022-03-08 16:44:29 +01:00

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"# CDS: Numerical Methods Assignments\n",
"\n",
"- See lecture notes and documentation on Brightspace for Python and Jupyter basics. If you are stuck, try to google or get in touch via Discord.\n",
"\n",
"- Solutions must be submitted via the Jupyter Hub.\n",
"\n",
"- Make sure you fill in any place that says `YOUR CODE HERE` or \"YOUR ANSWER HERE\".\n",
"\n",
"## Submission\n",
"\n",
"1. Name all team members in the the cell below\n",
"2. make sure everything runs as expected\n",
"3. **restart the kernel** (in the menubar, select Kernel$\\rightarrow$Restart)\n",
"4. **run all cells** (in the menubar, select Cell$\\rightarrow$Run All)\n",
"5. Check all outputs (Out[\\*]) for errors and **resolve them if necessary**\n",
"6. submit your solutions **in time (before the deadline)**"
]
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"team_members = \"Kees van Kempen, Koen Vendrig\""
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"source": [
"## Eigenvalues and Eigenvectors\n",
"\n",
"In the following you will implement your own eigenvalue / eigenvector calculation routines based on the inverse power method and the iterated QR decomposition."
]
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"source": [
"import numpy as np\n",
"import math as m"
]
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"### Task 1\n",
"We start by implementing the inverse power method, which calculates the eigenvector corresponding to an eigenvalue which is closest to a given parameter $\\sigma$. In detail, you should implement a Python function $\\text{inversePower(A, sigma, eps)}$, which takes as input the $n \\times n$ square matrix $A$, the parameter $\\sigma$, as well as some accuracy $\\varepsilon$. It should return the eigenvector $\\mathbf{v}$ (corresponding to the eigenvalue which is closest to $\\sigma$) and the number of needed iteration steps.\n",
"\t\n",
"To do so, implement the following algorithm. Start by setting up the needed input:\n",
"\n",
"\\begin{align}\n",
" B &= \\left( A - \\sigma \\mathbf{1} \\right)^{-1} \\\\\n",
" \\mathbf{b}^{(0)} &= (1,1,1,...)\n",
"\\end{align}\n",
"\n",
"where $\\mathbf{b}^{(0)}$ is a vector with $n$ entries. Then repeat the following and increase $k$ each iteration until the error $e$ is smaller than $\\varepsilon$:\n",
"\n",
"\\begin{align}\n",
" \\mathbf{b}^{(k)} &= B \\cdot \\mathbf{b}^{(k-1)} \\\\\n",
" \\mathbf{b}^{(k)} &= \\frac{\\mathbf{b}^{(k)}}{|\\mathbf{b}^{(k)}|} \\\\\n",
" e &= \\sqrt{ \\sum_{i=0}^n \\left(|b_i^{(k-1)}| - |b_i^{(k)}|\\right)^2 }\n",
"\\end{align}\n",
"\n",
"Return the last vector $\\mathbf{b}^{(k)}$ and the number of needed iterations $k$. \n",
"\n",
"Verify your implementation by calculating all the eigenvectors of the matrix below and comparing them to the ones calculated by $\\text{numpy.linalg.eig()}$. Then implement a unit test for your function.\n",
"\n",
"\\begin{align*}\n",
" A = \\begin{pmatrix}\n",
" 3 & 2 & 1\\\\ \n",
" 2 & 3 & 2\\\\\n",
" 1 & 2 & 3\n",
" \\end{pmatrix}.\n",
"\\end{align*}"
]
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"source": [
"def inversePower(A, sigma, eps):\n",
" \"\"\"\n",
" Estimates the eigenvectors of matrix A by the inverse power method.\n",
"\n",
" Args:\n",
" A: an n x n matrix\n",
" sigma: an initial guess for an eigenvector\n",
" eps: the desired accuracy\n",
"\n",
" Returns:\n",
" A tuple (b, k) is returned, containing:\n",
" b: the eigenvector b corresponding to the eigenvalue\n",
" closests to sigma after k iterations\n",
" k: the number of iterations done\n",
" \n",
" See also:\n",
" https://www.sciencedirect.com/topics/mathematics/inverse-power-method\n",
" \"\"\"\n",
" \n",
" # Does https://johnfoster.pge.utexas.edu/numerical-methods-book/LinearAlgebra_EigenProblem1.html help?\n",
" \n",
" # A should be n x n.\n",
" n = len(A)\n",
" assert len(A.shape) == 2 and A.shape[0] == A.shape[1]\n",
" \n",
" # Setup some initial values.\n",
" #B = np.linalg.inv(A - sigma*np.ones(n))\n",
" B = np.linalg.inv(A - sigma*np.eye(n))\n",
" #B = 1/(A - sigma*np.ones(n))\n",
" #B = 1/(A - sigma*np.eye(n))\n",
" b = np.ones(n)\n",
" k = 0\n",
" e = 0\n",
" \n",
" while e > eps or k == 0:\n",
" b_prev = b.copy()\n",
" k += 1\n",
" \n",
" b = B @ b\n",
" b /= np.sqrt(b @ b) # although b = np.linalg.norm(b) could be used\n",
" e = np.sqrt(np.sum( (np.abs(b_prev) - np.abs(b))**2) )\n",
" \n",
" return b, k"
]
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"# Use this cell for your own testing ...\n",
"\n",
"A = np.array([[3, 2, 1], [2, 3, 2], [1, 2, 3]])\n",
"\n",
"sigma_list = [.03, 6., 1.99999989999999]\n",
"#sigma = 0.03\n",
"\n",
"for sigma in sigma_list:\n",
" print(\"For sigma =\", sigma)\n",
" b, k = inversePower(A, sigma, 1e-6)\n",
" print(\"b =\", b)\n",
" lam = np.dot(A, b) / b\n",
" print(\"lam =\", lam)\n",
" print()\n",
" print()\n",
"\n",
"print(np.linalg.eig(A)[0])"
]
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"source": [
"def test_inversePower():\n",
" A = np.array([[3, 2, 1], [2, 3, 2], [1, 2, 3]])\n",
" \n",
"test_inversePower()"
]
},
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"source": [
"### Task 2 \n",
"\n",
"Next you will need to implement the tri-diagonalization scheme following Householder. To this end implement a Python function $\\text{tridiagonalize(A)}$ which takes a symmetric matrix $A$ as input and returns a tridiagonal matrix $T$ of the same dimension. Therefore, your algorithm should execute the following steps:\n",
"\t\n",
"First use an assertion to make sure $A$ is symmetric. Then let $k$ run from $0$ to $n-1$ and repeat the following:\n",
"\n",
"\\begin{align}\n",
" q &= \\sqrt{ \\sum_{j=k+1}^n \\left(A_{j,k}\\right)^2 } \\\\\n",
" \\alpha &= -\\operatorname{sgn}(A_{k+1,k}) \\cdot q \\\\\n",
" r &= \\sqrt{ \\frac{ \\alpha^2 - A_{k+1,k} \\cdot \\alpha }{2} } \\\\\n",
" \\mathbf{v} &= \\mathbf{0} \\qquad \\text{... vector of dimension } n \\\\\n",
" v_{k+1} &= \\frac{A_{k+1,k} - \\alpha}{2r} \\\\\n",
" v_{k+j} &= \\frac{A_{k+j,k}}{2r} \\quad \\text{for } j=2,3,\\dots,n \\\\\n",
" P &= \\mathbf{1} - 2 \\mathbf{v}\\mathbf{v}^T \\\\\n",
" A &= P \\cdot A \\cdot P\n",
"\\end{align}\n",
"\n",
"At the end return $A$ as $T$.\n",
"\n",
"Apply your routine to the matrix $A$ defined in task 1 as well as to a few random (but symmetric) matrices of different dimension $n$.\n",
"\n",
"Hint: Use $\\text{np.outer()}$ to calculate the *matrix* $\\mathbf{v}\\mathbf{v}^T$ needed in the definition of the Housholder transformation matrix $P$. "
]
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"source": [
"def tridiagonalize(A):\n",
" \"\"\"\n",
" Tridiagonalizes a given matrix following the Householder method.\n",
" Args:\n",
" A: NxN matrix\n",
" Returns:\n",
" The matrix A, but now tridiagonalized\n",
" \"\"\"\n",
" assert A.shape[0] == A.shape[1] and len(A.shape) == 2\n",
" n = len(A)\n",
" for k in range(n-1):\n",
" q = m.sqrt(np.sum(A[k+1:n,k]**2))\n",
" alpha = -1*np.sign(A[k+1,k])*q\n",
" r = m.sqrt((alpha**2 - A[k+1,k]*alpha)/2)\n",
" v = np.zeros(n)\n",
" v[k+1] = (A[k+1,k]-alpha)/(2*r)\n",
" print(np.shape(v),np.shape(A),np.shape(r),np.shape(A[k+2:n]/(2*r)))\n",
" v[k+2:n] = A[k+2:n,k]/(2*r)\n",
" P = np.identity(n)-(2*np.outer(v,v))\n",
" A = np.dot(np.dot(P,A),P)\n",
" return A"
]
},
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"source": [
"B = np.array([[3,2,1],[2,3,2],[1,2,3]])\n",
"\n",
"tridiagonalize(B)"
]
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"source": [
"### Task 3\n",
"\n",
"Implement the $QR$ decomposition based diagonalization routine for tri-diagonal matrices $T$ in Python as a function $\\text{QREig(T, eps)}$. It should take a tri-diagonal matrix $T$ and some accuracy $\\varepsilon$ as inputs and should return all eigenvalues in the form of a vector $\\mathbf{d}$. By making use of the $QR$ decomposition as implemented in Numpy's $\\text{numpy.linalg.qr()}$ the algorithm is very simple and reads:\n",
"\n",
"Repeat the following until the error $e$ is smaller than $\\varepsilon$:\n",
"\n",
"\\begin{align}\n",
" Q \\cdot R &= T^{(k)} \\qquad \\text{... do this decomposition with the help of Numpy!} \\\\\n",
" T^{(k+1)} &= R \\cdot Q \\\\\n",
" e &= |\\mathbf{d_1}| \n",
"\\end{align}\n",
"\n",
"where $T^{(0)} = T$ and $\\mathbf{d_1}$ is the first sub-diagonal of $T^{(k+1)}$ at each iteration step $k$. At the end return the main-diagonal of $T^{(k+1)}$ as $\\mathbf{d}$. \n",
"\n",
"Implement a unit test for your function based on the matrix $A$ defined in task 1. You will need to tri-diagonalize it first."
]
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"def QREig(T, eps):\n",
" \"\"\"\n",
" Follows the method of the QR decomposition based diagonalization routine for tridiagonalized matrices. The matrix T is\n",
" diagonalized, resulting in all diagonal elements being an eigenvalue.\n",
" Args:\n",
" T: a already tridiagonalized matrix\n",
" eps: the desired accuracy\n",
" Returns:\n",
" A one dimensional array with the eigenvalues of the matrix T\n",
" \"\"\"\n",
" e = eps + 1\n",
" while e > eps:\n",
" Q,R = np.linalg.qr(T)\n",
" T = np.matmul(R,Q)\n",
" e = np.sum(np.absolute(np.diag(T,k=1)))\n",
" #print(np.diag(T))\n",
" return np.diag(T)"
]
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"source": [
"A_tridiag = tridiagonalize(A)\n",
"print(A_tridiag)"
]
},
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"def test_QREig():\n",
" \"\"\"\n",
" Tests the QREig function for the matrix A_tridiag (defined in task 2) and eps = 0.001\n",
" \"\"\"\n",
" eps = 0.001\n",
" x = QREig(A_tridiag,eps)\n",
" print(x)\n",
"\n",
"test_QREig()\n"
]
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"source": [
"### Task 4\n",
"\n",
"With the help of $\\text{QREig(T, eps)}$ you can now calculate all eigenvalues. Then you can calculate all corresponding eigenvectors with the help of $\\text{inversePower(A, sigma, eps)}$, by setting $\\sigma$ to approximately the eigenvalues you found (you should add some small random noise to $\\sigma$ in order to avoid singularity issues in the inversion needed for the inverse power method). \n",
"\n",
"Apply this combination of functions to calculate all eigenvalues and eigenvectors of the matrix $A$ defined in task 1."
]
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"\"\"\"\n",
"Eigenvalues and -vectors calculated numerically for matrix A using functions from previous tasks.\n",
"\"\"\"\n",
"eps = 0.001\n",
"T = QREig(A_tridiag,eps)\n",
"for i in range(len(T)):\n",
" sigma = T[i] + np.random.normal(0,0.01,1)\n",
" x = inversePower(A_tridiag,sigma,eps)\n",
" print(x)"
]
},
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"source": [
"### Task 5\n",
"\n",
"Test your eigenvalue / eigenvector algorithm for larger random (but symmetric) matrices."
]
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"\"\"\"\n",
"Completely 'random' and totally not self-made matrices C and D are being tridiagonalized and its eigenvalues and -vectors are\n",
"calculated.\n",
"\"\"\"\n",
"C = np.array([[5,4,3,2,1]\n",
" ,[3,4,5,1,2]\n",
" ,[5,1,4,2,3]\n",
" ,[3,2,1,5,1]\n",
" ,[5,4,3,2,1]])\n",
"#print(C)\n",
"C_tridiag = tridiagonalize(C)\n",
"T = QREig(C_tridiag,eps)\n",
"for i in range(len(T)):\n",
" sigma = T[i] + np.random.normal(0,0.01,1)\n",
" x = inversePower(C_tridiag,sigma,eps)\n",
" print(x)\n",
"\n",
"D = np.array([[6,4,3,2,1]\n",
" ,[3,3,5,1,2]\n",
" ,[5,1,4,2,2]\n",
" ,[3,1,1,5,6]\n",
" ,[1,4,7,2,3]])\n",
"#print(C)\n",
"C_tridiag = tridiagonalize(C)\n",
"T = QREig(C_tridiag,eps)\n",
"for i in range(len(T)):\n",
" sigma = T[i] + np.random.normal(0,0.01,1)\n",
" x = inversePower(C_tridiag,sigma,eps)\n",
" print(x)"
]
}
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