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Unformatted text preview: Chapter 2: Matrices and Determinants March 16, 2009 1 Linear transformations Lecture 7 Definition 1.1. Let X and Y be nonempty sets. A function from X to Y is a rule f : X → Y such that each element x in X is assigned a unique element y in Y , written as y = f ( x ); the element y is called the image of x under f , and the element x is called the preimage of f ( x ). Functions are also called maps , or mappings , or transformations . Definition 1.2. A function T : R n → R m is called a linear transformation if for any vectors u , v in R n and scalar c , (a) T ( u + v ) = T ( u ) + T ( v ), (b) T ( c u ) = cT ( u ). Example 1.1. (a) The function T : R 2 → R 2 , defined by T ( x 1 ,x 2 ) = ( x 1 + 2 x 2 ,x 2 ), is a linear transformation, see Figure 1 (1,0) x x y y (0,0) (0,1) (1,1) (0,0) (1,0) (2,1) (3,1) Figure 1: The geometric shape under a linear transformation. (b) The function T : R 2 → R 3 , defined by T ( x 1 ,x 2 ) = ( x 1 +2 x 2 , 3 x 1 +4 x 2 , 2 x 1 +3 x 2 ), is a linear transformation. (c) The function T : R 3 → R 2 , defined by T ( x 1 ,x 2 ,x 3 ) = ( x 1 + 2 x 2 + 3 x 3 , 3 x 1 + 2 x 2 + x 3 ), is a linear transformation. Example 1.2. The transformation T : R n → R m by T ( x ) = A x , where A is an m × n matrix, is a linear transformation. Example 1.3. The map T : R n → R n , defined by T ( x ) = λ x , where λ is a constant, is a linear transformation, and is called the dilation by λ . 1 Example 1.4. The refection T : R 2 → R 2 about a straight line through the origin is a linear transformation. Example 1.5. The rotation T : R 2 → R 2 about an angle θ is a linear transformation, see Figure 3. θ O θ θ y x u T(u+v) T(u) T(v) u+v v Figure 2: Rotation about an angle θ . Proposition 1.3. Let T : R n → R m be a linear transformation. Then for any vectors v 1 , v 2 ,..., v k in R n and scalars c 1 ,c 2 ,...,c k , we have T ( c 1 v 1 + v 2 + ··· + c k v k ) = c 1 T ( v 1 ) + c 2 T ( v 2 ) + ··· + c k T ( v k ) . In particular, T ( ) = . Theorem 1.4. Let T : R n → R m be a linear transformation. Given vectors v 1 , v 2 ,..., v k in R n . (a) If v 1 , v 2 ,..., v k are linearly dependent, then T ( v 1 ) ,T ( v 2 ) ,...,T ( v k ) are linearly dependent. (b) If T ( v 1 ) ,T ( v 2 ) ,...,T ( v k ) are linearly independent, then v 1 , v 2 ,..., v k are linearly indepen dent. 2 Standard matrix of linear transformation Let T : R n → R m be a linear transformation. Consider the following vectors e 1 = 1 . . . , e 2 = 1 . . . , ..., e n = . . . 1 in the coordinate axis of R n . It is clear that for every vector x = x 1 x 2 ....
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This note was uploaded on 01/28/2011 for the course MATH 113 taught by Professor Beifangchan during the Fall '08 term at HKUST.
 Fall '08
 BeifangChan
 Calculus, Determinant, Transformations, Matrices, Sets

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