lecture-3.ipynb

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     "source": [
      "Lecture 3: Scalar product, orthogonality, matrix rank, singular value decomposition"
     ]
    },
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     "source": [
      "## Syllabus\n",
      "**Week 2:** Matrices, vectors, norms, ranks\n"
     ]
    },
    {
     "cell_type": "markdown",
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     "source": [
      "##Recap of the previous lecture\n",
      "- Norms are measures of smallness, used to compute the accuracy\n",
      "- Definition of $1$, $p$ and Euclidean norms\n",
      "- Matrix norms\n",
      "- $L_1$ norm (Manhattan distance) is used in compressed sensing as a surrogate for sparsity."
     ]
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     "source": [
      "## Today lecture\n",
      "Today we will talk about:\n",
      "- Scalar products and unitary (orthogonal) matrices\n",
      "- Rank of the matrix\n",
      "- Singular value decomposition (SVD) and low-rank approximation"
     ]
    },
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     "source": [
      "## Scalar product\n",
      "If norm is a measure of distances, then the scalar product takes angle into account.  \n",
      "The scalar product is defined as\n",
      "$$\n",
      "   (x, y) = \\sum_{i=1}^n \\overline{x}_i y_i,\n",
      "$$\n",
      "where $\\overline{x}$ denotes the *complex conjugate* of $x$. The Euclidean norm is then\n",
      "$$\n",
      "   \\Vert x \\Vert^2 = (x, x),\n",
      "$$\n",
      "or it is said the the norm is **induced** by the scalar product.  "
     ]
    },
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     "source": [
      "**Remark** For the angle between two vectors is defined as\n",
      "$$\n",
      "   \\cos \\phi = \\frac{(x, y)}{\\Vert x \\Vert \\Vert y \\Vert} \n",
      "$$"
     ]
    },
    {
     "cell_type": "markdown",
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     "source": [
      "An important property of the scalar product is the **Cauchy-Bunyakovski inequality**:\n",
      "$$\n",
      "    |(x, y)| \\leq \\Vert x \\Vert \\Vert y \\Vert,\n",
      "$$\n",
      "and thus the angle between two vectors is defined properly."
     ]
    },
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     "source": [
      "The scalar product can be written as a matrix-by-matrix product  \n",
      "$$\n",
      "  (x, y) = x^* y,\n",
      "$$\n",
      "where $^*$ is a **conjugate transpose** of the matrix:  \n",
      "$$\n",
      "B = A^*, \\quad B_{ij} = \\overline{A_{ji}}.\n",
      "$$"
     ]
    },
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     "source": [
      "## Norm conservation\n",
      "For stability it is really important that the error does not grow. Suppose that you approximately get a vector,  \n",
      "$$\n",
      "  \\Vert x - \\widehat{x} \\Vert \\leq \\varepsilon.\n",
      "$$\n",
      "Then the final result is (some) linear transformation of $x$:  \n",
      "$$\n",
      "   y = Ux, \\quad \\widehat{y} = U \\widehat{x}.\n",
      "$$\n",
      "If we want to estimate a difference between $\\widehat{y}$ and $y$:  \n",
      "$$\n",
      "   \\Vert y - \\widehat{y} \\Vert = \\Vert U ( x - \\widehat{x}) \\Vert \\leq \\Vert U \\Vert \\varepsilon.\n",
      "$$\n",
      "The question is for which kind of matrices the length of the vector **will not change**. For the euclidean norm this produces a very important class of matrices: **unitary** (or orthogonal in the real case) matrices."
     ]
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     "source": [
      "## Unitary (orthogonal) matrices\n",
      "Let $U$ be an $n \\times r$ matrix, and $\\Vert U z \\Vert = \\Vert z \\Vert$ for all $z$. This can happen if and only if  \n",
      "$$\n",
      "   U^* U = I,\n",
      "$$\n",
      "where $I$ is an **identity matrix**\n",
      "Indeed, $$\\Vert Uz \\Vert^2 = (Uz, Uz) = (Uz)^* Uz = z^* (U^* U) z = z^* z,$$ \n",
      "which can only hold if $U^* U = I$."
     ]
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     "source": [
      "## Unitary matrices\n",
      "In the real case, when $U^* = U^{\\top}$, the matrix is called orthogonal.  \n",
      "Are there many unitary matrices? First of all, **a product of two unitary matrices is a unitary matrix:**  \n",
      "$$(UV)^* UV = V^* (U^* U) V = V^* V = I,$$\n",
      "thus if we give some non-trivial examples of unitary matrices, we will be able to get any unitary transformation."
     ]
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     "source": [
      "## Examples of unitary matrices\n",
      "There are two important classes of unitary matrices:\n",
      "1. Householder matrices\n",
      "2. Givens (Jacobi) matrices"
     ]
    },
    {
     "cell_type": "markdown",
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     "source": [
      "## Householder matrices\n",
      "Householder matrix is the matrix of the form  \n",
      "$$H = I - 2 vv^*,$$\n",
      "where $v$ is an $n \\times 1$ matrix and $v^* v = 1$. Can you show that $H$ is unitary?  It is also a reflection. <img src=\"householder.jpeg\">  \n",
      "A simple proof: $H^* H = (I - 2 vv^*)(I - 2 v v^*) = I$"
     ]
    },
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     "cell_type": "markdown",
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     "source": [
      "## Givens(Jacobi) matrix\n",
      "A Givens matrix is a matrix  \n",
      "$$\n",
      "    A = \\begin{bmatrix}\n",
      "          \\cos \\alpha & \\sin \\alpha \\\\\n",
      "          -\\sin \\alpha & \\cos \\alpha\n",
      "        \\end{bmatrix},\n",
      "$$\n",
      "which is a rotation. For a general case, we select two $(i, j)$ planes and rotate only in those:\n",
      "$$\n",
      "    x'_i = \\cos \\alpha x_i + \\sin \\alpha x_j, \\quad x'_j = -\\sin \\alpha x_i + \\cos\\alpha x_j,\n",
      "$$\n",
      "with all other $x_i$ remain unchanged."
     ]
    },
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     "source": [
      "## Summary on unitary matrices\n",
      "- Unitary matrices preserve the norm\n",
      "- There are two \"basic\" classes of unitary matrices, Householder and Givens.\n",
      "- Every unitary matrix can be represented as a product of those."
     ]
    },
    {
     "cell_type": "markdown",
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     "source": [
      "## Matrix and linear spaces\n",
      "A matrix can be considered as a sequence of vectors, its columns:\n",
      "$$\n",
      "   A = [a_1, \\ldots, a_m], \n",
      "$$\n",
      "where $a_m \\in \\mathbb{C}^n$.  \n",
      "A matrix-by-vector product is equivalent to taking a linear combination of those vectors  \n",
      "$$\n",
      "   y =  Ax, \\quad \\Longleftrightarrow y = a_1 x_1 + a_2 x_2 + \\ldots +a_m x_m.\n",
      "$$"
     ]
    },
    {
     "cell_type": "markdown",
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     },
     "source": [
      "## Linear dependence\n",
      "The vectors are called linearly dependent, if there exist non-zero coefficients $x_i$ such that\n",
      "$$\\sum_i a_i x_i = 0,$$\n",
      "or in the matrix form,\n",
      "$$\n",
      "   Ax = 0, \\quad \\Vert x \\Vert \\ne 0.\n",
      "$$\n",
      "In this case, we say that the matrix $A$ has non-trivial **nullspace**."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
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     "source": [
      "## Dimension of a linear space\n",
      "A **linear space** is defined as all possible vectors of the form \n",
      "$$\n",
      "   \\sum_{i=1}^m a_i x_i, \n",
      "$$\n",
      "where $x_i$ are some coefficients and $a_i, i = 1, \\ldots, m$ are given vectors. In the matrix form, the linear space is a collection of the vectors of the form  \n",
      "$$A x.$$\n",
      "This set is also called the **range** of the matrix.\n"
     ]
    },
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     "source": [
      "## Matrix rank\n",
      "What is a matrix rank?  A matrix rank is defined as the maximal number of linearly independent columns in a matrix, or the **dimension of its column space**.  \n",
      "You can also use linear combination of rows to define the rank.  "
     ]
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     "source": [
      "** Theorem**  \n",
      "The dimension of the column space of the matrix is equal to the dimension of the row space of the matrix."
     ]
    },
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     "source": [
      "## Skeleton decomposition\n",
      "A very useful representation for computation of the matrix rank is the **skeleton decomposition**, which can be graphically represented as follows:  \n",
      "<img src=\"cross-pic.png\">\n",
      "or in the matrix form\n",
      "$$\n",
      "   A = U \\widehat{A}^{-1} V^{\\top},\n",
      "$$\n",
      "where $U$ are some $r$ columns of $A$, $V^{\\top}$ are some rows of $A$, $\\widehat{A}$ is the submatrix on the intersection, which should be **nonsingular**."
     ]
    },
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     "source": [
      "**Remark.**  \n",
      "An inverse of the matrix $P$ the matrix $Q = P^{-1}$ such that  \n",
      "$ P Q = QP = I$.  \n",
      "If the matrix is square and has full rank (i.e. equal to $n$) then the inverse exists."
     ]
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     "source": [
      "## Proof for the skeleton decomposition\n",
      "Let $U$ be the $r$ columns based on the submatrix $\\widehat{A}$. Then they are linearly independent. Take any other column $u$ of $A$. Then it is a linear combination of the columns of $U$, i.e.  \n",
      "$$u = U x$$.  \n",
      "These are $n$ equations. We take $r$ of those corresponding to the rows that contain $\\widehat{A}$ and get the equation  \n",
      "$$\\widehat{u} = \\widehat{A} x,$$  therefore  \n",
      "$$x = \\widehat{A}^{-1} \\widehat{u},$$ and that gives us the result."
     ]
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     "source": [
      "## A closer look\n",
      "Any rank-$r$ matrix can be written in the form\n",
      "$$A = U \\widehat{A}^{-1} V^{\\top},$$\n",
      "where $U$ is $n \\times r$, $V$ is $m \\times r$ and $\\widehat{A}$ is $r \\times r$, or \n",
      "$$\n",
      "   A = U' V'^{\\top}.\n",
      "$$\n",
      "So, every rank-$r$ matrix can be written as a product of a \"tall\" matrix $U'$ by a long matrix $V'$. "
     ]
    },
    {
     "cell_type": "markdown",
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     "source": [
      "In the index form, it is  \n",
      "$$\n",
      "   a_{ij} = \\sum_{\\alpha=1}^r u_{i \\alpha} v_{j \\alpha}.\n",
      "$$\n",
      "For rank 1 we have\n",
      "$$\n",
      "   a_{ij} = u_i v_j,\n",
      "$$\n",
      "i.e. it is a separation of indices and rank-$r$ is a sum of rank-$1$ matrices!"
     ]
    },
    {
     "cell_type": "markdown",
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     "source": [
      "###Storage\n",
      "It is interesting to note, that for the rank-$r$ matrix \n",
      "$$A = U V^{\\top}$$\n",
      "only $U$ and $V$ can be stored, which gives us $(n+m) r$ parameters, so it can be used for compression. We can also compute matrix-by-vector product much faster."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "n = 1000\n",
      "r = 10\n",
      "u = np.random.randn(n, r)\n",
      "v = np.random.randn(n, r)\n",
      "a = u.dot(v.T) #u.dot(v.T.conj())\n",
      "x = np.random.randn(n)\n",
      "%timeit a.dot(x)\n",
      "%timeit u.dot(v.T.dot(x))"
     ],
     "language": "python",
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     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 559 \u00b5s per loop\n",
        "100000 loops, best of 3: 11.9 \u00b5s per loop"
       ]
      },
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       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
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     "prompt_number": 1
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     "source": [
      "We can also try to compute the matrix rank using the built-in ```np.linalg.matrix_rank``` function"
     ]
    },
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     "cell_type": "code",
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     "input": [
      "#Computing matrix rank\n",
      "import numpy as np\n",
      "n = 50 \n",
      "a = np.ones((n, n))\n",
      "print 'Rank of the matrix:', np.linalg.matrix_rank(a)\n",
      "b = a + 1e-10 * np.random.randn(n, n)\n",
      "print 'Rank of the matrix:', np.linalg.matrix_rank(b)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Rank of the matrix: 1\n",
        "Rank of the matrix: 50\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
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     "source": [
      "### Instability of the matrix rank\n",
      "For any rank-$r$ matrix $A$ with $r < \\min(m, n)$ there is a matrix $B$ such that its rank rank is equal to $\\min(m, n)$ and\n",
      "$$\n",
      " \\Vert A - B \\Vert = \\varepsilon.\n",
      "$$\n",
      "So, does this mean that numerically matrix rank has no meaning? (I.e., small perturbations lead to full rank)!  \n",
      "The solution is to compute the best **rank-r** approximation to a matrix."
     ]
    },
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     "source": [
      "## Singular value decomposition\n",
      "To compute low-rank approximation, we need to compute **singular value decomposition**.  \n",
      "Any matrix $A$ can be written as a product of three matrices:  \n",
      "$$\n",
      "   A = U \\Sigma V^*,\n",
      "$$\n",
      "where $U$ is an $n \\times K$ unitary matrix, $V$ is an $m \\times K$ unitary matrix, $K = \\min(m, n)$ $\\Sigma$ is a diagonal matrix with non-negative elements $\\sigma_1 \\geq  \\ldots, \\geq \\sigma_K$ on the diagonal. \n",
      "\n",
      "Computation of the best rank-$r$ approximation is equivalent to setting $\\sigma_{r+1}= 0, \\ldots, \\sigma_K = 0$. The error is then determined by the omitted singular value!  \n",
      "$$\n",
      "   \\min_{A_r} \\Vert A - A_r \\Vert = \\sigma_{r+1},\n",
      "$$\n",
      "that is why it is important to look at the decay of the singular vectors."
     ]
    },
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     "source": [
      "## Low-rank approximation via SVD\n",
      "$\\Vert A - A_r \\Vert = \\Vert U \\Sigma V^* - A_r \\Vert = \\Vert \\Sigma - U^* A_r V \\Vert$,  \n",
      "where we used that for spectral norm (and for the Frobenius as well) we have  \n",
      "\n",
      "$$\\Vert X Q \\Vert = \\Vert X \\Vert$$\n",
      "for any unitary $Q$ and  any matrix $X$. This is called **unitary equivalence** of the spectral norm (and Frobenius as well).  \n",
      "What is left is to note that the best rank-$r$ approximation of the diagonal matrix is its subdiagonal.  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Computing the SVD is tricky; we will talk about the algorithms hidden in this computation later. But we already can do this in Python!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Computing matrix rank\n",
      "import numpy as np\n",
      "n = 50 \n",
      "a = np.ones((n, n))\n",
      "print 'Rank of the matrix:', np.linalg.matrix_rank(a)\n",
      "b = a + 1e-5 * np.random.randn(n, n)\n",
      "print 'Rank of the matrix:', np.linalg.matrix_rank(b)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Rank of the matrix: 1\n",
        "Rank of the matrix: 50\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "u, s, v = np.linalg.svd(b)\n",
      "print s[1]\n",
      "r = 1\n",
      "u1 = u[:, :r]\n",
      "s1 = s[:r]\n",
      "v1 = v[:r, :]\n",
      "a1 = u1.dot(np.diag(s1).dot(v1))\n",
      "print np.linalg.norm(a - a1, 'fro')/np.linalg.norm(a)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
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     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.000137482954279\n",
        "1.8585046137e-06\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "We can use SVD to compute approximations of **function-related** matrices, i.e. the matrices of the form \n",
      "$$a_{ij} = f(x_i, y_j),$$\n",
      "where $f$ is a certain function, and $x_i, \\quad i = 1, \\ldots, n$ and $y_j, \\quad j = 1, \\ldots, m$ are some **one-dimensional grids**."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import prettyplotlib as ppl\n",
      "n = 1000\n",
      "a = [[1.0/(i+j+1) for i in xrange(n)] for j in xrange(n)] #Hilbert matrix \n",
      "a = np.array(a)\n",
      "u, s, v = np.linalg.svd(a)\n",
      "fig, ax = ppl.subplots(1, 1)\n",
      "ax.semilogy(s[1:30])\n",
      "#We have very good low-rank approximation of it!"
     ],
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        "[<matplotlib.lines.Line2D at 0x10f813a50>]"
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Wp6UzNT7B7JJ6RQEgIuKm3KpytmUfZtbQJB6xTCfMTx8vrQAQEemBmqYGduQe\no7KhlqcnLmJ45CCzS3KbAkBEpIecTie20hzev3qBpWMmMjluFGOiBhPSz9sPTvYMBYCISC8VVlfw\n6bU8cirLqGqqIyVmOBMGjyAtdrhPB4ICQETEg2431pFdVU5OZRk5VeXcbqonNWYYaYNHMCF2BAlR\ng33mYXMKABERL6pqrPtrGGRXlXGnqYGk6DgiQyPoHxr21z8DQsPoHxpO/5Cwv1k/MCyCpOh4r9Sm\nABAR6UOVDbUU11bS0NJMQ0sz9a7XhpYm49XRut5YjggJY+M9Vq/UogAQEQlSvjYnsIiI+CgFgIhI\nkFIAiIgEKQWAiEiQUgCIiAQpBYCISJDqzZSQ3ZEMfB+IBVZhBM5PgEEYM4rt9PLni4hIF7zdArgC\nPN1ueQWQADQSxPMH22w2s0vwqkA+vkA+NtDxBZvuBsA2oAw432H9MiALyAWe6cZ+0oBDwD8D3+nm\nZwecQP9HGMjHF8jHBjq+YNPdANiO8cu+vVDgZdf6ycAaYBKwDvglMLqT/RQBla6fg3ciThERH9Dd\nAPgUqOiwbh6QB9iBJmA3RhfPa8D/AkqAeOD/ATMxWgjvAF8EfgXYelW5iIj0ijvPArIAe4GpruVH\nMX6Zb3AtrwXSgY0eqKuYzlsQIiLStRKM66zd0ptRQN58Wpt/z8wsIuIHejMKqBhIbLecSBCP7BER\nCWQW/nYUUBhw2bU+AjiDcRFYREQCyC6MvqUGoBB4yrV+OZCNcTH4Xz3wOe4OK/U3duAccBo4bm4p\nHtHZ8OB44EMgB/gTMNiEujyls+P7EUZL97TrT8fRcf4iEfgYuAhcAP7JtT5Qzl9Xx/cjAuP8DQCO\nYXzxvgS84Frvt+cvFCNILEA4gdmiuIJxggLFYowRXu1/Qf4b8C+un58BNvd1UR7U2fFtAr5rTjke\nNRKY4fo5GuOL3CQC5/x1dXyBcv4ABrpew4CjwL24ef586VlAXQ0rDTS+OgtbT3Q2PPhhYIfr5x3A\nV/q0Is/q7PggMM7hNYwvWQDVQCbG4ItAOX9dHR8ExvkDqHW9RmB8ga7AzfPnSwGQgNG91KqIwBsN\n5AT+C+M5SBvu8l5/NQKj2wTX6wgTa/GWjcBZYCt+1MT+DBaMls4xAvP8WTCO76hrOVDOXwhGyJXR\n1t3l1vnzpQAIhkmAF2H8Q1wO/HeMLoZA5iTwzut/YDzkcAZQCvwfc8vptWjgbeB/AHc6/F0gnL9o\n4C2M46sSb30LAAABHklEQVQmsM6fA+M4xgCfA+7r8Pd3PX++FADBMKy01PV6HXgXo9sr0JRh9L8C\njALKTazFG8pp+4+1Bf8+h+EYv/xfA95zrQuk89d6fK/TdnyBdP5aVQEfALNx8/z5UgCcAFJpG1a6\nGthjZkEeNhDjMdgAUcAX+PuH6wWCPcCTrp+fpO0/XqAY1e7nR/Dfc9gPowvkEvBSu/WBcv66Or5A\nOX9Daeu+igSWYoxq8uvz5+lhpb4kGaO/7gzGsLRAOL7W4cGNtA0Pjse4zuF3w9A60fH4voExh8U5\njD7k9/DfPvJ7MboQzvC3QyID5fx1dnzLCZzzNxU4hXF854DvudYHyvkTERERERERERERERERERER\nERERERERERERERGA/w8f/Jy6Dv6RogAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ee42950>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Now we can do something more interesting, like function approximation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "n = 128\n",
      "t = np.linspace(0, 5, n)\n",
      "x, y = np.meshgrid(t, t)\n",
      "f = 1.0 / (x + y ** 2 + 0.5)\n",
      "u, s, v = np.linalg.svd(f, full_matrices=False)\n",
      "r = 10\n",
      "u = u[:, :r]\n",
      "s = s[:r]\n",
      "v = v[:r, :] #Mind the transpose here!\n",
      "fappr = u.dot(np.diag(s).dot(v))\n",
      "er = np.linalg.norm(fappr - f, 'fro') / np.linalg.norm(f, 'fro')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "And do some 3D plotting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "fig = plt.figure(figsize=(12, 5))\n",
      "ax = fig.add_subplot(131, projection='3d')\n",
      "ax.plot_surface(x, y, f)\n",
      "ax.set_title('Original function')\n",
      "ax = fig.add_subplot(132, projection='3d')\n",
      "ax.plot_surface(x, y, fappr)\n",
      "ax.set_title('Approximated function')\n",
      "ax = fig.add_subplot(133, projection='3d')\n",
      "ax.plot_surface(x, y, fappr - f)\n",
      "ax.set_title('Approximation error with rank=%d, err=%3.1e' % (r, er))\n",
      "fig.subplots_adjust()\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AAJFIBNXV1QDiqXUUhgbiionT6ewmSOg4p+BVNqUmPPIBy5z8oqqq1owhFouh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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f4eb110>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Linear factor analysis & low-rank\n",
      "Consider a linear factor model, \n",
      "\n",
      "$$y = Ax, $$ where $y$ is a vector of length $n$, and $x$ is a vector of length $r$.  \n",
      "The data is organizes as samples: we observe vectors  \n",
      "$$y_1, \\ldots, y_T,$$\n",
      "then the factor model can be written as  \n",
      "$$\n",
      "  Y = AX,\n",
      "$$\n",
      "where $Y$ is $n \\times T$, $A$ is $n \\times r$ and $X$ is $r \\times T$. This is exactly a rank-$r$ model: it tells us that the vector lie in a small subspace!  \n",
      "We also can use SVD to recover this subspace (but not the independent components). Principal component analysis can be done by SVD!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Take home message\n",
      "- Scalar product\n",
      "- Unitary/orthogonal matrices and norm conservation\n",
      "- Matrix rank definition\n",
      "- Skeleton approximation and dyadic representation of a rank-$r$ matrix\n",
      "- Singular value decomposition"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "##### Questions?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "def css_styling():\n",
      "    styles = open(\"./styles/custom.css\", \"r\").read()\n",
      "    return HTML(styles)\n",
      "css_styling()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "html": [
        "<link href='http://fonts.googleapis.com/css?family=Fenix' rel='stylesheet' type='text/css'>\n",
        "<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n",
        "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' type='text/css'>\n",
        "<style>\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
        "    }\n",
        "    div.cell{\n",
        "        /*width:80%;*/\n",
        "        /*margin-left:auto !important;\n",
        "        margin-right:auto;*/\n",
        "    }\n",
        "    h1 {\n",
        "        font-family: 'Alegreya Sans', sans-serif;\n",
        "    }\n",
        "    h2 {\n",
        "        font-family: 'Fenix', serif;\n",
        "    }\n",
        "    h3{\n",
        "\t\tfont-family: 'Fenix', serif;\n",
        "        margin-top:12px;\n",
        "        margin-bottom: 3px;\n",
        "       }\n",
        "\th4{\n",
        "\t\tfont-family: 'Fenix', serif;\n",
        "       }\n",
        "    h5 {\n",
        "        font-family: 'Alegreya Sans', sans-serif;\n",
        "    }\t   \n",
        "    div.text_cell_render{\n",
        "        font-family: 'Alegreya Sans',Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
        "        line-height: 1.2;\n",
        "        font-size: 160%;\n",
        "        /*width:70%;*/\n",
        "        /*margin-left:auto;*/\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    .CodeMirror{\n",
        "            font-family: \"Source Code Pro\";\n",
        "\t\t\tfont-size: 90%;\n",
        "    }\n",
        "/*    .prompt{\n",
        "        display: None;\n",
        "    }*/\n",
        "    .text_cell_render h1 {\n",
        "        font-weight: 200;\n",
        "        font-size: 50pt;\n",
        "\t\tline-height: 100%;\n",
        "        color:#CD2305;\n",
        "        margin-bottom: 0.5em;\n",
        "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "    }\t\n",
        "    .text_cell_render h5 {\n",
        "        font-weight: 300;\n",
        "        font-size: 16pt;\n",
        "        color: #CD2305;\n",
        "        font-style: italic;\n",
        "        margin-bottom: .5em;\n",
        "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "    }\n",
        "    \n",
        "    .warning{\n",
        "        color: rgb( 240, 20, 20 )\n",
        "        }  \n",
        "</style>\n",
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
        "                           extensions: [\"AMSmath.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
        "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
        "                }\n",
        "        });\n",
        "</script>\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "<IPython.core.display.HTML at 0x11892b6d0>"
       ]
      }
     ],
     "prompt_number": 63
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 ]
}