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     10 
     11 <div id="Linear transformations"><h2 id="Linear transformations">Linear transformations</h2></div>
     12 <p>
     13 definitions:
     14 </p>
     15 <ul>
     16 <li>
     17 transformation, function, mapping: rule assigning to each vector in \(\Re^n\) a vector \(T(x)\) in \(\Re^m\)
     18 
     19 <li>
     20 domain: set \(\Re^n\)
     21 
     22 <li>
     23 codomain: set \(\Re^m\)
     24 
     25 <li>
     26 image: vector T(x)
     27 
     28 <li>
     29 range: set of all images T(x)
     30 
     31 </ul>
     32 
     33 <p>
     34 a projection transformation happens if you go to a lower dimension (e.g. \(x_3\) becomes 0). a shear transformation happens if a 2D square is tilted sideways into a parallelogram.
     35 </p>
     36 
     37 <p>
     38 a transformation T is linear if:
     39 </p>
     40 <ol>
     41 <li>
     42 \(T(u + v) = T(u) + T(v)\) for all \(u,v \in \text{Domain}(T)\)
     43 
     44 <li>
     45 \(T(cu) = cT(u)\) for all scalars c and all \(u \in \text{Domain}(T)\)
     46 
     47 </ol>
     48 
     49 <p>
     50 linear transformations preserve operations of vector addition and scalar multiplication.
     51 </p>
     52 
     53 <p>
     54 if T is a linear transformation, then:
     55 </p>
     56 <ul>
     57 <li>
     58 \(T(0) = 0)\)
     59 
     60 <li>
     61 \(T(cu + dv) = cT(u) + dT(v)\)
     62 
     63 <li>
     64 \(T(c_1 v_2 + \dots + c_p v_p) = c_1 T(v_1) + \dots + c_p T(v_p)\) (superposition principle)
     65 
     66 </ul>
     67 
     68 <p>
     69 given scalar r, and \(T: \Re^2 \rightarrow \Re^2\) by \(T(x) = rx\)
     70 </p>
     71 <ul>
     72 <li>
     73 contraction: when \(0 \leq r \leq 1\)
     74 
     75 <li>
     76 dilation: when \(r &gt; 1\)
     77 
     78 </ul>
     79 
     80 <p>
     81 every linear transformation \(\Re^n \rightarrow \Re^m\) is a matrix transformation \(x \mapsto Ax\). 
     82 </p>
     83 
     84 <p>
     85 \(A = [[T(e_1) \dots T(e_n)]\), where \(e_j\) is the jth column of the identity matrix in \(\Re^n\)
     86 </p>
     87 
     88 <p>
     89 geometric linear transformations of \(\Re^2\):
     90 </p>
     91 
     92 <p>
     93 <img src="img/geo-reflections.png" alt="Reflections" /> <img src="img/geo-contract-shears.png" alt="Contractions/expansions and shears" /> <img src="img/geo-projections.png" alt="Projections" />
     94 </p>
     95 
     96 <p>
     97 types of mappings:
     98 </p>
     99 <ul>
    100 <li>
    101 \(T: \Re^n \rightarrow \Re^m\) is 'onto' \(\Re^m\) if <em>each</em> b in \(\Re^m\) is the image of <em>at least one</em> x in \(\Re^n\).
    102 
    103 <li>
    104 \(T: \Re^n \rightarrow \Re^m\) is one-to-one if <em>each</em> b in \(\Re^m\) is the image of <em>max one</em> x in \(\Re^n\).
    105 
    106 <ul>
    107 <li>
    108 so if \(T(x) = 0\) only has the trivial solution
    109 
    110 </ul>
    111 </ul>
    112 
    113 <p>
    114 for mapping \(T: \Re^n \rightarrow \Re^m\) and standard matrix \(A\):
    115 </p>
    116 <ul>
    117 <li>
    118 T maps \(\Re^n\) onto \(\Re^m\) iff columns of matrix span \(\Re^m\)
    119 
    120 <li>
    121 T is one-to-one iff columns of matrix are linearly independent.
    122 
    123 </ul>
    124 
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