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Unformatted text preview: Linear transformations: the basics A linear transformation is a homomorphism from one vector space to another (over the same field). That is, a linear transformation is a function from one F-space V to an F-space W (possibly the same as V ) which preserves the vector space structure. Given the field F , this structure consists of vector addition and scalar multiplication. It is probably correct to say that these transformations are the main object of study in linear algebra. Heres the proper definition: DEFINITION (Linear transformation) Suppose that F is a field and V and W are vector spaces over F . A linear transformation from V to W is a function T : V- W such that: 1. For any ~v 1 and ~v 2 in V , T ( ~v 1 + ~v 2 ) = T~v 1 + T~v 2 . (That is, T preserves or respects vector addition); and 2. For any ~v V and scalar F , T ( ~v ) = T~v . (That is, T preserves, or respects, scalar multiplication.) In particular, such a T takes as input a vector ~v V and puts out as output some ~w = T~v W . A linear transformation is also known as a linear map . In the (very impor- tant) special case where V = W , a linear transformation from V to itself is usually called a linear operator on V . Another important special case is where V is any vector space over F and W = F ; in this case a linear transformation is known as a linear functional on V . Before proceeding to examples, we make some comments: 1. Please do not say that a linear transformation T is closed under addition (or closed under scalar multiplication). Thats just bad grammar. It is not even false; it makes no sense. It is like saying My hat purples. 2. Note that I have written T~v rather than T ( ~v ) when I didnt need the brackets; this is standard. 3. A significant part of the structure of a vector space V is the zero vector ~ V ; surely we should insist that a linear T should take zero to zero. (In fact we would insist on that, except that it is easy to prove. Note that ~ W + T ~ V = T ~ V = T ( ~ V + ~ V ) = T ~ V + T ~ V . By cancellation in the space W , we have T ~ V = ~ W .) 4. A similar comment holds for T (- ~v ); as- ~v =- 1 F ~v , this must be- T~v . The basic kind of example is when V = F n for some n and W = F m , and A is an m n matrix with entries from F . We let T A : V- W be defined by 1 T A ~v = A~v matrix multiplication. To see that this is linear, suppose ~v 1 and ~v 2 are in F n ; by distributivity of matrix multiplication, T A ( ~v 1 + ~v 2 ) = A ( ~v 1 + ~v 2 ) = A~v 1 + A~v 2 = T A ~v 1 + T A ~v 2 , so T A preserves vector addition. Also for any ~v F n , and F , T A ( ~v ) = A ( ~v ) = A~v = T A ~v by a basic (unfortunately unnamed) property of matrix multiplication....
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