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Lecture notes from university.
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      1 == Linear transformations ==
      2 definitions:
      3     * transformation, function, mapping: rule assigning to each vector in $\Re^n$ a vector $T(x)$ in $\Re^m$
      4     * domain: set $\Re^n$
      5     * codomain: set $\Re^m$
      6     * image: vector T(x)
      7     * range: set of all images T(x)
      8 
      9 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.
     10 
     11 a transformation T is linear if:
     12     i) $T(u + v) = T(u) + T(v)$ for all $u,v \in \text{Domain}(T)$
     13     ii) $T(cu) = cT(u)$ for all scalars c and all $u \in \text{Domain}(T)$
     14 
     15 linear transformations preserve operations of vector addition and scalar multiplication.
     16 
     17 if T is a linear transformation, then:
     18     * $T(0) = 0)$
     19     * $T(cu + dv) = cT(u) + dT(v)$
     20     * $T(c_1 v_2 + \dots + c_p v_p) = c_1 T(v_1) + \dots + c_p T(v_p)$ (superposition principle)
     21 
     22 given scalar r, and $T: \Re^2 \rightarrow \Re^2$ by $T(x) = rx$
     23     * contraction: when $0 \leq r \leq 1$
     24     * dilation: when $r > 1$
     25 
     26 every linear transformation $\Re^n \rightarrow \Re^m$ is a matrix transformation $x \mapsto Ax$. 
     27 
     28 $A = [[T(e_1) \dots T(e_n)]$, where $e_j$ is the jth column of the identity matrix in $\Re^n$
     29 
     30 geometric linear transformations of $\Re^2$:
     31 
     32 {{file:img/geo-reflections.png|Reflections}} {{file:img/geo-contract-shears.png|Contractions/expansions and shears}} {{file:img/geo-projections.png|Projections}}
     33 
     34 types of mappings:
     35     * $T: \Re^n \rightarrow \Re^m$ is 'onto' $\Re^m$ if _each_ b in $\Re^m$ is the image of _at least one_ x in $\Re^n$.
     36     * $T: \Re^n \rightarrow \Re^m$ is one-to-one if _each_ b in $\Re^m$ is the image of _max one_ x in $\Re^n$.
     37         * so if $T(x) = 0$ only has the trivial solution
     38 
     39 for mapping $T: \Re^n \rightarrow \Re^m$ and standard matrix $A$:
     40     * T maps $\Re^n$ onto $\Re^m$ iff columns of matrix span $\Re^m$
     41     * T is one-to-one iff columns of matrix are linearly independent.
     42