matrices/slides.tex
author Puneeth Chaganti <punchagan@fossee.in>
Fri, 05 Nov 2010 21:01:23 +0530
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% Created 2010-10-12 Tue 14:28
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\documentclass[presentation]{beamer}
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\usepackage[latin1]{inputenc}
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\usepackage[T1]{fontenc}
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\usepackage{fixltx2e}
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\usepackage{graphicx}
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\usepackage{longtable}
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\usepackage{float}
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\usepackage{wrapfig}
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\usepackage{soul}
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\usepackage{t1enc}
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\usepackage{textcomp}
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\usepackage{marvosym}
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\usepackage{wasysym}
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\usepackage{latexsym}
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\usepackage{amssymb}
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\usepackage{hyperref}
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\tolerance=1000
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\usepackage[english]{babel} \usepackage{ae,aecompl}
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\usepackage{mathpazo,courier,euler} \usepackage[scaled=.95]{helvet}
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\usepackage{listings}
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\lstset{language=Python, basicstyle=\ttfamily\bfseries,
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commentstyle=\color{red}\itshape, stringstyle=\color{darkgreen},
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showstringspaces=false, keywordstyle=\color{blue}\bfseries}
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\providecommand{\alert}[1]{\textbf{#1}}
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\title{Matrices}
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\author{FOSSEE}
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\date{}
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\usetheme{Warsaw}\usecolortheme{default}\useoutertheme{infolines}\setbeamercovered{transparent}
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\begin{document}
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\maketitle
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\begin{frame}
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\frametitle{Outline}
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\label{sec-1}
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\begin{itemize}
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\item Creating Matrices
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\begin{itemize}
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\item using direct data
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\item converting a list
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\end{itemize}
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\item Matrix operations
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\item Inverse of matrix
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\item Determinant of matrix
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\item Eigen values and Eigen vectors of matrices
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\item Norm of matrix
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\item Singular Value Decomposition of matrices
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\end{itemize}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Creating a matrix}
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\label{sec-2}
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\begin{itemize}
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\item Creating a matrix using direct data
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\end{itemize}
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\begin{verbatim}
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   In []: m1 = matrix([1, 2, 3, 4])
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\end{verbatim}
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\begin{itemize}
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\item Creating a matrix using lists
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\end{itemize}
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\begin{verbatim}
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   In []: l1 = [[1,2,3,4],[5,6,7,8]]
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   In []: m2 = matrix(l1)
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\end{verbatim}
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\begin{itemize}
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\item A matrix is basically an array
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\end{itemize}
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\begin{verbatim}
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   In []: m3 = array([[5,6,7,8],[9,10,11,12]])
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\end{verbatim}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Matrix operations}
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\label{sec-3}
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\begin{itemize}
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\item Element-wise addition (both matrix should be of order \texttt{mXn})
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\begin{verbatim}
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     In []: m3 + m2
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\end{verbatim}
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\item Element-wise subtraction (both matrix should be of order \texttt{mXn})
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\begin{verbatim}
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     In []: m3 - m2
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\end{verbatim}
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\end{itemize}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Matrix Multiplication}
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\label{sec-4}
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\begin{itemize}
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\item Matrix Multiplication
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\begin{verbatim}
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     In []: m3 * m2
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     Out []: ValueError: objects are not aligned
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\end{verbatim}
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\item Element-wise multiplication using \texttt{multiply()}
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\begin{verbatim}
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     multiply(m3, m2)
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\end{verbatim}
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\end{itemize}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Matrix Multiplication (cont'd)}
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\label{sec-5}
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\begin{itemize}
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\item Create two compatible matrices of order \texttt{nXm} and \texttt{mXr}
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\begin{verbatim}
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     In []: m1.shape
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\end{verbatim}
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\begin{itemize}
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\item matrix m1 is of order \texttt{1 X 4}
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\end{itemize}
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\item Creating another matrix of order \texttt{4 X 2}
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\begin{verbatim}
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     In []: m4 = matrix([[1,2],[3,4],[5,6],[7,8]])
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\end{verbatim}
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\item Matrix multiplication
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\begin{verbatim}
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     In []: m1 * m4
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\end{verbatim}
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\end{itemize}
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\end{frame}
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\begin{frame}
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\frametitle{Recall from \texttt{array}}
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\label{sec-6}
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\begin{itemize}
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\item The functions
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\begin{itemize}
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\item \texttt{identity(n)} - 
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      creates an identity matrix of order \texttt{nXn}
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\item \texttt{zeros((m,n))} - 
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      creates a matrix of order \texttt{mXn} with 0's
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\item \texttt{zeros\_like(A)} - 
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      creates a matrix with 0's similar to the shape of matrix \texttt{A}
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\item \texttt{ones((m,n))}
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      creates a matrix of order \texttt{mXn} with 1's
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\item \texttt{ones\_like(A)}
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      creates a matrix with 1's similar to the shape of matrix \texttt{A}
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\end{itemize}
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\end{itemize}
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  Can also be used with matrices
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{More matrix operations}
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\label{sec-7}
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  Transpose of a matrix
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\begin{verbatim}
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   In []: m4.T
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\end{verbatim}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Exercise 1 : Frobenius norm \& inverse}
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\label{sec-8}
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  Find out the Frobenius norm of inverse of a \texttt{4 X 4} matrix.
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\begin{verbatim}
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\end{verbatim}
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  The matrix is
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\begin{verbatim}
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   m5 = matrix(arange(1,17).reshape(4,4))
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\end{verbatim}
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\begin{itemize}
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\item Inverse of A,
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\begin{itemize}
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\item $A^{-1} = inv(A)$
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\end{itemize}
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\item Frobenius norm is defined as,
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\begin{itemize}
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\item $||A||_F = [\sum_{i,j} abs(a_{i,j})^2]^{1/2}$
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\end{itemize}
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\end{itemize}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Exercise 2: Infinity norm}
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\label{sec-9}
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  Find the infinity norm of the matrix \texttt{im5}
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\begin{verbatim}
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\end{verbatim}
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\begin{itemize}
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\item Infinity norm is defined as,
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       $max([\sum_{i} abs(a_{i})^2])$
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\end{itemize}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{\texttt{norm()} method}
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\label{sec-10}
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\begin{itemize}
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\item Frobenius norm
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\begin{verbatim}
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     In []: norm(im5)
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\end{verbatim}
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\item Infinity norm
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\begin{verbatim}
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     In []: norm(im5, ord=inf)
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\end{verbatim}
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\end{itemize}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Determinant}
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\label{sec-11}
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  Find out the determinant of the matrix m5
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\begin{verbatim}
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\end{verbatim}
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\begin{itemize}
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\item determinant can be found out using
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\begin{itemize}
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\item \texttt{det(A)} - returns the determinant of matrix \texttt{A}
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\end{itemize}
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\end{itemize}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{eigen values \& eigen vectors}
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\label{sec-12}
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  Find out the eigen values and eigen vectors of the matrix \texttt{m5}.
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\begin{verbatim}
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\end{verbatim}
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\begin{itemize}
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\item eigen values and vectors can be found out using
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\begin{verbatim}
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     In []: eig(m5)
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\end{verbatim}
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    returns a tuple of \emph{eigen values} and \emph{eigen vectors}
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\item \emph{eigen values} in tuple
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\begin{itemize}
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\item \texttt{In []: eig(m5)[0]}
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\end{itemize}
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\item \emph{eigen vectors} in tuple
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\begin{itemize}
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\item \texttt{In []: eig(m5)[1]}
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\end{itemize}
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\item Computing \emph{eigen values} using \texttt{eigvals()}
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\begin{verbatim}
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     In []: eigvals(m5)
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\end{verbatim}
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\end{itemize}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Singular Value Decomposition (\texttt{svd})}
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\label{sec-13}
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    $M = U \Sigma V^*$
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\begin{itemize}
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\item U, an \texttt{mXm} unitary matrix over K.
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\item $\Sigma$
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        , an \texttt{mXn} diagonal matrix with non-negative real numbers on diagonal.
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\item $V^*$
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        , an \texttt{nXn} unitary matrix over K, denotes the conjugate transpose of V.
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\item SVD of matrix \texttt{m5} can be found out as,
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\end{itemize}
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\begin{verbatim}
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     In []: svd(m5)
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\end{verbatim}
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\end{frame}
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\begin{frame}
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\frametitle{Summary}
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\label{sec-14}
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\begin{itemize}
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\item Matrices
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\begin{itemize}
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\item creating matrices
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\end{itemize}
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\item Matrix operations
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\item Inverse (\texttt{inv()})
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\item Determinant (\texttt{det()})
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\item Norm (\texttt{norm()})
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\item Eigen values \& vectors (\texttt{eig(), eigvals()})
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\item Singular Value Decomposition (\texttt{svd()})
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\end{itemize}
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\end{frame}
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\begin{frame}
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\frametitle{Thank you!}
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\label{sec-15}
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  \begin{block}{}
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  \begin{center}
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  This spoken tutorial has been produced by the
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  \textcolor{blue}{FOSSEE} team, which is funded by the 
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  \end{center}
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  \begin{center}
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    \textcolor{blue}{National Mission on Education through \\
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      Information \& Communication Technology \\ 
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      MHRD, Govt. of India}.
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  \end{center}  
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  \end{block}
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\end{frame}
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\end{document}