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     11 <div id="Matrix operations"><h2 id="Matrix operations">Matrix operations</h2></div>
     12 <p>
     13 \(a_{ij}\) is the entry in the ith row and jth column of A
     14 </p>
     15 
     16 <p>
     17 diagonal entries are \(a_{11}\), \(a_{22}\), etc. and form the main diagonal. if non-diagonal entries are zero, then it's a diagonal matrix.
     18 </p>
     19 
     20 <p>
     21 equal matrices have same size <em>and</em> their corresponding entries are equal.
     22 </p>
     23 
     24 <div id="Matrix operations-Sums and scalar multiples"><h3 id="Sums and scalar multiples">Sums and scalar multiples</h3></div>
     25 <p>
     26 sum A+B: sum corresponding entries in A and B.
     27 </p>
     28 
     29 <p>
     30 scalar multiple \(rA\) is matrix whose columns are r times the corresponding columns in A (with r scalar).
     31 </p>
     32 
     33 <p>
     34 the usual rules of algebra apply to sums and scalar multiples of matrices.
     35 </p>
     36 
     37 <p>
     38 when matrix B multiplies vector x, it transforms x into vector \(Bx\). if \(Bx\) is multiplied by A, the result is \(A(Bx)\). \(A(Bx)\) is produced from x by composition of mappings, which can be represented as multiplication by a single matrix AB.
     39 </p>
     40 
     41 <p>
     42 \(A(Bx) = (AB)x\)
     43 </p>
     44 
     45 <p>
     46 \(AB = A \begin{bmatrix} b_1 &amp; b_2 &amp; \dots &amp; b_p \end{bmatrix} = \begin{bmatrix} Ab_1 &amp; Ab_2 &amp; \dots &amp; Ab_p \end{bmatrix}\)
     47 </p>
     48 
     49 <p>
     50 A is matrix, B is matrix with columns \(b_1 \dots b_p\).
     51 </p>
     52 
     53 <p>
     54 each column of AB is a linear combination of columns of A using weights from corresponding column of B. AB has the same number of rows as A and same number of columns as B. if the number of columns of A does not match number of rows of B, the product is undefined. in general, AB ≠ BA.
     55 </p>
     56 
     57 <p>
     58 if product AB is defined, then:
     59 </p>
     60 
     61 
     62 <p>
     63 \((AB)_{ij} = a_{i1} b_{1j} + a_{i2} b_{2j} + \dots + a_{in} b_{nj}\)
     64 </p>
     65 
     66 <p>
     67 \(row_i (AB) = row_i (A) \times B\)
     68 </p>
     69 
     70 <p>
     71 a nifty trick: if multiplying matrix m×n by matrix n×o, the product will be a matrix m×o.
     72 </p>
     73 
     74 <div id="Matrix operations-Powers of a matrix"><h3 id="Powers of a matrix">Powers of a matrix</h3></div>
     75 <p>
     76 \(A^k = \underbrace{A \dots A}_{k}\)
     77 </p>
     78 
     79 <p>
     80 with \(A\) an n × n matrix and k a positive integer.
     81 </p>
     82 
     83 <div id="Matrix operations-Transpose of a matrix"><h3 id="Transpose of a matrix">Transpose of a matrix</h3></div>
     84 <p>
     85 a matrix \(A'\) whose columns are made up of the corresponding rows of \(A\)
     86 </p>
     87 
     88 <p>
     89 properties:
     90 </p>
     91 <ul>
     92 <li>
     93 \((A^T)^T = A\)
     94 
     95 <li>
     96 \((A+B)^T = A^T + B^T\)
     97 
     98 <li>
     99 \((rA)^T = rA^T\) with r a scalar
    100 
    101 <li>
    102 \((AB)^T = B^T A^T\)$
    103 
    104 </ul>
    105 
    106 <p>
    107 the transpose of a product of matrices == product of their transposes in reverse order
    108 </p>
    109 
    110 <div id="Matrix operations-Inverse of a matrix"><h3 id="Inverse of a matrix">Inverse of a matrix</h3></div>
    111 <p>
    112 invertible (singular) if there is same size matrix C such that \(CA = I\) and \(AC = I\) where I is the n × n identity matrix.
    113 </p>
    114 
    115 <p>
    116 identity matrix: a matrix where the diagonals are all 1.
    117 </p>
    118 
    119 <p>
    120 C is uniquely determined by A, so: \(A^{-1} A = I\).
    121 </p>
    122 
    123 <p>
    124 let \(A = \begin{bmatrix} a &amp; b \\ c &amp; d \end{bmatrix}.\) if \(ad - bc \ne 0\) then \(A^{-1} = \frac{1}{ad - bc} \begin{bmatrix} d &amp; -b \\ -c &amp; a \end{bmatrix}\)
    125 </p>
    126 
    127 <p>
    128 determinant: \(\det A = ad - bc\)
    129 </p>
    130 
    131 <p>
    132 if A is invertible (determinant is not 0), then for each \(b \in \Re^n\) the solution of \(Ax = b\) is \(A^{-1} b\).
    133 </p>
    134 
    135 <p>
    136 properties of inverse:
    137 </p>
    138 <ul>
    139 <li>
    140 \((A^{-1})^{-1} = A\)
    141 
    142 <li>
    143 \((AB)^{-1} = B^{-1} A^{-1}\) (watch out for order!)
    144 
    145 <li>
    146 \((A^T)^{-1} = (A^{-1})^T\)
    147 
    148 </ul>
    149 
    150 <p>
    151 finding \(A^{-1}\):
    152 </p>
    153 <ul>
    154 <li>
    155 Row reduce augmented matrix \(\begin{bmatrix} A &amp; I \end{bmatrix}\).
    156 
    157 <li>
    158 if A is row equivalent to I, then \(\begin{bmatrix} A &amp; I \end{bmatrix}\) is row equivalent to \(\begin{bmatrix} I &amp; A^{-1} \end{bmatrix}\)
    159 
    160 <li>
    161 otherwise, A doesn't have an inverse.
    162 
    163 </ul>
    164 
    165 <div id="Matrix operations-Elementary matrices"><h3 id="Elementary matrices">Elementary matrices</h3></div>
    166 <p>
    167 elementary matrix: obtained by performing single elementary row operation on identity matrix
    168 </p>
    169 
    170 <p>
    171 if elementary row operation is performed on m × n matrix A, result is EA, with E an m × m matrix obtained by performing same row operation on \(I_m\)
    172 </p>
    173 
    174 <p>
    175 inverse of any elementary matrix E is of same type that transforms E back into I.
    176 </p>
    177 
    178 <p>
    179 an n² matrix A is only invertible if A is row equivalent to \(I_n\). any sequence of elementary operations reducing A to \(I_n\) also transforms \(I_n\) into \(A^{-1}\).
    180 </p>
    181 
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