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Transcript
Section 4.3 Properties of Linear Transformations from Rn to Rm ONE-TO-ONE TRANSFORMATIONS A linear transformation T: Rn → Rm is said to be one-to-one if T maps distinct vectors (points) in Rn to distinct vectors (points) in Rm. THREE EQUIVALENT STATEMENTS Theorem 4.3.1: If A is an n×n matrix and TA: Rn → Rn is multiplication by A, then the following statements are equivalent. (a) A is invertible. (b) The range of TA is Rn. (c) TA is one-to-one. NOTE: This extends our “big theorem” from Chapter 2. See Theorem 4.3.4 on page 206. INVERSE OF A 1-1 LINEAR OPERATOR If TA: Rn → Rn is a one-to-one linear operator, then [T] = A is invertible. The linear operator TA1 : R R inverse of TA. n n is called the Notation: The inverse of T is denoted by T−1 and its standard matrix is [T−1] = [T]−1 PROPERTIES OF LINEAR TRANSFORMATIONS Theorem 4.3.2: A transformation T: Rn → Rm is linear if and only if the following relationships hold for all vectors u and v in Rn and every scalar c. (a) T(u + v) = T(u) + T(v) (b) T(cu) = c T(u) STANDARD BASIS VECTORS IN Rn The standard basis vectors in Rn are given by e1 (1, 0, 0, , 0) e 2 (0, 1, 0,, 0) e n (0, 0, 0,, 1) A THEOREM Theorem 4.3.3: If T: Rn → Rm is a linear transformation, and e1, e2, . . . , en are the standard basis vectors for Rn, then the standard matrix for T is [T ] [T (e1 ) |T (e 2 ) ||T (e n ) ] EIGENVALUES AND EIGENVECTORS OF A LINEAR OPERATOR If T: Rn → Rn is a linear operator, then a scalar λ is called an eigenvalue of T if there is a nonzero x in Rn such that T(x) = λx Those nonzero vectors x that satisfy this equation are called the eigenvectors of T corresponding to λ.