pset 3
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psets/pset2.ipynb
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psets/pset2.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0f2a9965",
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"metadata": {},
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"source": [
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"# 18.085 Problem Set 2\n",
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"\n",
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"Due Friday **March 3** at 1pm."
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]
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},
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{
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"cell_type": "markdown",
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"id": "d9e8ae37",
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"metadata": {},
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"source": [
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"## Problem 1\n",
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"\n",
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"**(a)** The eigenvalues of a real *anti-Hermitian* matrix $A = -A^T$ must be \\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_\\_. Derive this by considering $\\overline{x^T} A x$ for an eigenvector $Ax = \\lambda x$, and demonstrate it numerically by constructing a random anti-Hermitian matrix `A = randn(n,n); A = A - A'` in Julia and looking at its eigenvalues `using LinearAlgebra; eigvals(A)` for `n=4` and `n=5`. (You might want to try the numerical experiment first if you aren't sure what the answer is.)\n",
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"\n",
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"**(b)** Suppose $A$ is a $3 \\times 3$ real-symmetric matrix with eigenvalues $\\lambda_1 = -2$, $\\lambda_2 = 3$, and $\\lambda_3 = -1$, with corresponding orthonormal eigenvectors $q_1, q_2, q_3$. In terms of these quantities, give the (full) SVD of $A$.\n",
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"\n",
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"**(c)** Construct a random $5 \\times 3$ matrix `A = randn(5, 3)` and form a related *real-symmetric* matrix `B = [ 0I A; A' 0I ]`, corresponding to\n",
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"$$\n",
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"B = \\begin{pmatrix} 0 & A \\\\ A^T & 0 \\end{pmatrix} .\n",
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"$$\n",
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"Compare the eigenvalues of $B$ (`eigvals(B)`) to the singular values of $A$ (`svdvals(A)`). What do you notice? Explain it by using the SVD $A = U \\Sigma V^T$ to construct eigenvalues and eigenvectors of $B$."
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]
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},
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{
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"cell_type": "markdown",
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"id": "88ac965b",
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"metadata": {},
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"source": [
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"## Problems 2, 3, etc: coming soon"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e1fca19a",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Julia 1.8.0",
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"language": "julia",
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"name": "julia-1.8"
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},
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"language_info": {
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"file_extension": ".jl",
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"mimetype": "application/julia",
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"name": "julia",
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"version": "1.8.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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