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#+BEAMER_FRAME_LEVEL: 1
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#+BEAMER_HEADER_EXTRA: \usetheme{Warsaw}\usecolortheme{default}\useoutertheme{infolines}\setbeamercovered{transparent}
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#+LaTeX_CLASS: beamer
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#+LaTeX_CLASS_OPTIONS: [presentation]
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#+LaTeX_HEADER: \usepackage[english]{babel} \usepackage{ae,aecompl}
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#+LaTeX_HEADER: \usepackage{mathpazo,courier,euler} \usepackage[scaled=.95]{helvet}
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#+LaTeX_HEADER: \usepackage{listings}
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#+LaTeX_HEADER:\lstset{language=Python, basicstyle=\ttfamily\bfseries,
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#+LaTeX_HEADER: commentstyle=\color{red}\itshape, stringstyle=\color{darkgreen},
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#+LaTeX_HEADER: showstringspaces=false, keywordstyle=\color{blue}\bfseries}
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#+TITLE: Matrices
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#+AUTHOR: FOSSEE
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#+EMAIL:
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#+DATE:
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#+DESCRIPTION:
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#+KEYWORDS:
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#+LANGUAGE: en
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#+OPTIONS: H:3 num:nil toc:nil \n:nil @:t ::t |:t ^:t -:t f:t *:t <:t
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#+OPTIONS: TeX:t LaTeX:nil skip:nil d:nil todo:nil pri:nil tags:not-in-toc
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* Outline
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- Creating Matrices
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- using direct data
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- converting a list
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- Matrix operations
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- Inverse of matrix
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- Determinant of matrix
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- Eigen values and Eigen vectors of matrices
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- Norm of matrix
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- Singular Value Decomposition of matrices
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* Creating a matrix
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- Creating a matrix using direct data
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: In []: m1 = matrix([1, 2, 3, 4])
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- Creating a matrix using lists
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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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- A matrix is basically an array
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: In []: m3 = array([[5,6,7,8],[9,10,11,12]])
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* Matrix operations
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- Element-wise addition (both matrix should be of order ~mXn~)
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: In []: m3 + m2
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- Element-wise subtraction (both matrix should be of order ~mXn~)
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: In []: m3 - m2
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* Matrix Multiplication
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- Matrix Multiplication
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: In []: m3 * m2
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: Out []: ValueError: objects are not aligned
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- Element-wise multiplication using ~multiply()~
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: multiply(m3, m2)
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* Matrix Multiplication (cont'd)
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- Create two compatible matrices of order ~nXm~ and ~mXr~
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: In []: m1.shape
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- matrix m1 is of order ~1 X 4~
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- Creating another matrix of order ~4 X 2~
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: In []: m4 = matrix([[1,2],[3,4],[5,6],[7,8]])
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- Matrix multiplication
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: In []: m1 * m4
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* Recall from ~array~
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- The functions
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- ~identity(n)~ -
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creates an identity matrix of order ~nXn~
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- ~zeros((m,n))~ -
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creates a matrix of order ~mXn~ with 0's
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- ~zeros_like(A)~ -
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creates a matrix with 0's similar to the shape of matrix ~A~
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- ~ones((m,n))~
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creates a matrix of order ~mXn~ with 1's
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- ~ones_like(A)~
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creates a matrix with 1's similar to the shape of matrix ~A~
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Can also be used with matrices
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* More matrix operations
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Transpose of a matrix
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: In []: m4.T
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* Exercise 1 : Frobenius norm \& inverse
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Find out the Frobenius norm of inverse of a ~4 X 4~ matrix.
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:
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The matrix is
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: m5 = matrix(arange(1,17).reshape(4,4))
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- Inverse of A,
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-
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#+begin_latex
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$A^{-1} = inv(A)$
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#+end_latex
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- Frobenius norm is defined as,
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-
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#+begin_latex
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$||A||_F = [\sum_{i,j} abs(a_{i,j})^2]^{1/2}$
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#+end_latex
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* Exercise 2: Infinity norm
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Find the infinity norm of the matrix ~im5~
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:
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- Infinity norm is defined as,
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#+begin_latex
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$max([\sum_{i} abs(a_{i})^2])$
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#+end_latex
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* ~norm()~ method
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- Frobenius norm
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: In []: norm(im5)
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- Infinity norm
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: In []: norm(im5, ord=inf)
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* Determinant
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Find out the determinant of the matrix m5
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:
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- determinant can be found out using
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- ~det(A)~ - returns the determinant of matrix ~A~
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* eigen values \& eigen vectors
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Find out the eigen values and eigen vectors of the matrix ~m5~.
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:
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- eigen values and vectors can be found out using
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: In []: eig(m5)
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returns a tuple of /eigen values/ and /eigen vectors/
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- /eigen values/ in tuple
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- ~In []: eig(m5)[0]~
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- /eigen vectors/ in tuple
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- ~In []: eig(m5)[1]~
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- Computing /eigen values/ using ~eigvals()~
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: In []: eigvals(m5)
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* Singular Value Decomposition (~svd~)
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#+begin_latex
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$M = U \Sigma V^*$
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#+end_latex
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- U, an ~mXm~ unitary matrix over K.
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-
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#+begin_latex
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$\Sigma$
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#+end_latex
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, an ~mXn~ diagonal matrix with non-negative real numbers on diagonal.
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-
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#+begin_latex
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$V^*$
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#+end_latex
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, an ~nXn~ unitary matrix over K, denotes the conjugate transpose of V.
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- SVD of matrix ~m5~ can be found out as,
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: In []: svd(m5)
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* Summary
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- Matrices
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- creating matrices
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- Matrix operations
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- Inverse (~inv()~)
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- Determinant (~det()~)
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- Norm (~norm()~)
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- Eigen values \& vectors (~eig(), eigvals()~)
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- Singular Value Decomposition (~svd()~)
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* Thank you!
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#+begin_latex
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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_latex
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