Chapter 6 Linear Transformations 6.1 Introductions to Linear Transformations • Function T that maps a vector space V into a vector space W: T : V mapping W , V ,W : vectorspace V: the domain of T W: the codomain of T 6-1 • Image of v under T: If v is in V and w is in W such that T ( v) w Then w is called the image of v under T . • the range of T: The set of all images of vectors in V. • the preimage of w: The set of all v in V such that T(v)=w. 6-2 • Notes: (1) A linear transformation is said to be operation preserving, because the same result occurs whether the operations of addition and scalar multiplication are performed before or after T. T (u v) T (u) T ( v) Addition in V Addition in W T (cu) cT (u) Scalar multiplication in V Scalar multiplication in W (2) A linear transformation T : V V from a vector space into itself is called a linear operator. 6-3 6-4 • Two simple linear transformations: Zero transformation: T :V W T ( v) 0, v V Identity transformation: T :V V T ( v) v, v V 6-5 6-6 6-7 6.2 The Kernel and Range a Linear Transformation 6-8 6-9 • Note: The kernel of T is sometimes called the nullspace of T. 6-10 T (x) Ax (a linear tr ansformati on T : R n R m ) Ker (T ) NS ( A) x | Ax 0, x R m (subspace of R m ) • Range of a linear transformation T: Let T : V W be a L.T . T hen theset of all vectorsw in W thatare images of vectors in V is called therange of T and is denotedby range(T ) range(T ) {T ( v) | v V } 6-11 • Notes: T : V W is a L.T. (1) Ker(T ) is subspace of V (2)range(T ) is subspace of W 6-12 • Note: Let T : R n R m be theL.T .given by T (x) Ax, then rank(T ) rank( A) nullity(T ) nullity( A) 6-13 6-14 • One-to-one: A functionT : V W is called one- to - oneif thepreimageof everyw in therange consistsof a single vector. T is one- to - oneiff for all u and v inV, T (u) T ( v) implies thatu v. one-to-one not one-to-one • Onto: A functionT : V W is said to be ontoif everyelement in w has a preimagein V (T is onto W when W is equal to the range of T.) 6-15 6-16 6-17 6-18 6.3 Matrices for Liner Transformations • Two representations of the linear transformation T:R3→R3 : (1)T ( x1, x2 , x3 ) (2x1 x2 x3 , x1 3x2 2x3 ,3x2 4x3 ) 2 1 1 x1 (2)T (x) Ax 1 3 2 x2 0 3 4 x3 • Three reasons for matrix representation of a linear transformation: – It is simpler to write. – It is simpler to read. – It is more easily adapted for computer use. 6-19 6-20 • Notes: (1) The standard matrix for the zero transformation from Rn into Rm is the mn zero matrix. (2) The standard matrix for the identity transformation from Rn into Rn is the nn identity matrix In • Composition of T1:Rn→Rm with T2:Rm→Rp : T ( v) T2 (T1 ( v)), v Rn T T2 T1 domain of T domain of T1 6-21 • Note: T1 T2 T2 T1 6-22 • Note: If the transformation T is invertible, then the inverse is unique and denoted by T–1 . 6-23 6-24 6-25 6.4 Transition Matrices and Similarty T :V V ( a L.T ). B {v1 , v2 ,, vn } ( a basis of V ), B' {w1 , w2 ,, wn } (a basis of V ) A T (v1 )B , T (v2 )B ,, T (vn )B A' T (w1 )B' , T (w2 )B' ,, T (wn )B' P w1 B , w2 B ,, wn B P1 v1 B' , v2 B' ,, vn B' ( matrixof T relativeto B) (matrixof T relativeto B' ) ( transition matrixfromB' to B ) ( transition matrixfromB to B' ) v B Pv B ' , vB ' P 1vB T ( v)B AvB T ( v)B' A' vB' 6-26 • Two ways to get from vB' to T (v) : B' indirect (1)(direct) A'[ v]B ' [T ( v)]B ' (2)(indirect) P 1 AP[ v]B ' [T ( v)]B ' A' P 1 AP direct 6-27 6-28 • Note: From the definition of similarity it follows that any tow matrices that represent the same linear transformation T : V V with respect to different based must be similar. 6-29 6.5 Applications of Linear Transformations 6-30 6-31 6-32 6-33 6-34