A linear transformation between two vector spaces and is a map such that the following hold:
1. for any vectors and in , and
2. for any scalar .
A linear transformation may or may not be injective or surjective. When and have the same dimension, it is possible for to be invertible, meaning there exists a such that . It is always the case that . Also, a linear transformation always maps lines to lines (or to zero).
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The main example of a linear transformation is given by matrix multiplication. Given an matrix , define , where is written as a column vector (with coordinates). For example, consider
(1)
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then is a linear transformation from to , defined by
(2)
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When and are finite dimensional, a general linear transformation can be written as a matrix multiplication only after specifying a vector basis for and . When and have an inner product, and their vector bases, and , are orthonormal, it is easy to write the corresponding matrix . In particular, . Note that when using the standard basis for and , the th column corresponds to the image of the th standard basis vector.
When and are infinite dimensional, then it is possible for a linear transformation to not be continuous. For example, let be the space of polynomials in one variable, and be the derivative. Then , which is not continuous because while does not converge.
Linear two-dimensional transformations have a simple classification. Consider the two-dimensional linear transformation
(3)
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(4)
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Now rescale by defining and . Then the above equations become
(5)
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where and , , , and are defined in terms of the old constants. Solving for gives
(6)
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so the transformation is one-to-one. To find the fixed points of the transformation, set to obtain
(7)
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This gives two fixed points, which may be distinct or coincident. The fixed points are classified as follows.