ICS 6N Computational Linear Algebra Vector Space

Transcription

1 ICS 6N Computational Linear Algebra Vector Space Xiaohui Xie University of California, Irvine Xiaohui Xie (UCI) ICS 6N 1 / 24

2 Vector Space Definition: A vector space is a non empty set V of objects called vectors on which we define two operations: addition and multiplication by a scalar, and they are subject to ten rules: 1) If u, v V, u + v V 2) u + v = v + u 3) (u + v) + w = u + (v + w) 4) There exists a zero vector 0 such that u + 0 = u 5) For each u V, there exists ( u) such that u + ( u) = 0 6) For any scalar c and u V, cu V 7) c(u + v) = cu = cv for any scalar c 8) (c + d)u = cu + du for any scalars c, d 9) c(du) = (cd)u for any scalars c,d 10) 1u = u Xiaohui Xie (UCI) ICS 6N 2 / 24

3 Vector space examples R n is a vector space. P n : the set of polynomial functions that can be written as f (t) = a 0 + a 1 t a n t n then P n is a vector space if we define: Addition: If f (t) = a 0 + a 1 t a n t n and f P n, and g(t) = b 0 + b 1 t b n t n and g P n, then f + g = a 0 + b 0 + (a 1 + b 1 )t (a n + b n )t n P n Multiplication by a scalar cf (t) = ca 0 + ca 1 t ca n t n P n Xiaohui Xie (UCI) ICS 6N 3 / 24

4 Subspaces Definition: A subset H of a vector space V is called a subspace if: a) 0 H and b) If x, y H, then x + y H and c) If x H, then rx H for a scalar r We can also easily see H is also a vector space by itself. Xiaohui Xie (UCI) ICS 6N 4 / 24

5 Examples 1) H = 0 is a subspace 2) H = span { u }, u R n is a subspace Xiaohui Xie (UCI) ICS 6N 5 / 24

6 Null Space of A Definition: the null space of m n matrix A is the set of all solutions to Ax = 0. Null(A) = { x R n : Ax = 0 } Null(A) is a subspace of R n It contains the zero vector. If x, y Null(A), does x + y Null(A)? If x Null(A), is = rx Null(A)? Xiaohui Xie (UCI) ICS 6N 6 / 24

7 Column Space of A Definition: the column space of m n matrix A is all the linear combinations of column vectors of A. Col(A) = span { a 1,..., a n } Col(A) is a subspace of R m It contains the zero vector. If x, y Col(A), does x + y Col(A)? If x Col(A), is = rx Col(A)? Xiaohui Xie (UCI) ICS 6N 7 / 24

8 How to describe a subspace? Span by a set x 1,..., x p V span { x 1,..., x p } = all linear combinations of x1,..., x p span { x 1,..., x p } is a subspace The spanning set of H is the set of vectors x 1,..., x p so that span{x 1,..., x p } = H Xiaohui Xie (UCI) ICS 6N 8 / 24

9 Example How to find a spanning set of Null(A) A = reduce it to echelon form Find solutions in parametric form with x 4 and x 5 free. x 3 = 2x 4 + 2x 5 x1 = 2x 2 + x 4 3x 5 Xiaohui Xie (UCI) ICS 6N 9 / 24

10 Represent the solution in vector form: x 1 2x 2 + x 4 3x x 2 x = x 3 x 4 = x 2 2x 4 + 2x 5 x 4 = x x x x 5 x We have the spanning set = x 2 u + x 4 v + x 5 w Null(A) = span { u, v, w } Xiaohui Xie (UCI) ICS 6N 10 / 24

11 Spanning set representation of null space Then number of vectors in the spanning set of Null(A) = number of free variables = n - number of pivot columns u, v, w are linearly independent. The only way of making x 2 u + x 4 v + x 5 w = 0 is if x 2 = x 4 = x 5 = 0 Null(A) = { 0 } if there is no free variables. Xiaohui Xie (UCI) ICS 6N 11 / 24

12 Column space The column space of m n matrix A A subspace of R m Col A = span{a 1,, a n } Col(A) = R m Ax = b has a solution for every b Xiaohui Xie (UCI) ICS 6N 12 / 24

13 Basis of a vector space Definition: v 1, v 2,... v r is a basis of vector space V if: a) V = span { v 1,..., v r } b) {v 1, v 2,... v r } is linearly independent Xiaohui Xie (UCI) ICS 6N 13 / 24

14 Linear independent Definition: v 1, v 2,... v r are linearly independent if c 1 v c r v r = 0 has only trivial solutions. In other words, v i cannot be written down as a linear combination of the rest of the preceding vectors for any i. This is v i c 1 v c i 1 v i 1 for any i = 1,..., r Xiaohui Xie (UCI) ICS 6N 14 / 24

15 Example Consider vectors in R 2 b 1 = [ ] 1, b 0 2 = [ ] 0, b 1 3 = [ ] 2 2 b 2 = 2b 1 + 2b 2 { b1, b 2 } is a basis for R 2 The augmented matrix of x 1 b 1 + x 2 b 2 + x 3 b 3 = 0 is [ ] has nontrivial solutions since x 3 is a free variable. The first two columns are pivot columns. Col A is the span of pivot columns. Xiaohui Xie (UCI) ICS 6N 15 / 24

16 Finding a basis of a column space Reduce matrix A to echelon form B = Find solutions of Ax = 0 (also Bx = 0) in parametric form x 5 = 0 x 3 = x 4 x 1 = 4x 2 2x 4 with x 2, x 4 free. With x 2 = 1, x 4 = 0: x 5 = 0, x 3 = 0, x 1 = 4 = 4a 1 + a 2 = 0 With x 2 = 0, x 4 = 1: x 5 = 0, x 3 = 1, x 1 = 2 2a 1 + a 3 + a 4 = 0 Any nonpivot column can be written as a linear combination of pivot columns. Xiaohui Xie (UCI) ICS 6N 16 / 24

17 Find basis of column space Reduce matrix to echelon form: A = B = Any nonpivot column can be written as a linear combination of pivot columns The pivot columns are linearly independent The pivot columns form a basis of the column space. Col(A) = span { a 1, a 2, a 3, a 4, a 5 } basis of Col A = { a 1, a 3, a 5 } basis of Col B = { b 1, b 3, b 5 } Xiaohui Xie (UCI) ICS 6N 17 / 24

18 Basis for Null(A): Number of vectors in the basis of Null(A) is equal to the number of free variables Basis for Col(A): Number of vectors in the basis of Col(A) is equal to the number of pivot columns which is equal to the number of basic variables Xiaohui Xie (UCI) ICS 6N 18 / 24

19 Dimension The dimension of V is the number of vectors in a basis of V dim(null(a)) = number of free variables = number of non-pivot columns dim(col(a)) = number of basic variables The rank of A is: r(a) = dim(col(a)) Xiaohui Xie (UCI) ICS 6N 19 / 24

20 Rank theorem For any mxn matrix A, r(a) + dim(null(a)) = n Xiaohui Xie (UCI) ICS 6N 20 / 24

21 Let A be an nxn matrix. If A is invertible, what is r(a)? r(a) = n. And it is called a full rank matrix in this case. If a matrix is invertible, the null space contains only the trivial solution and by definition: Null(A) = { 0 } dim(null(a)) = 0 Xiaohui Xie (UCI) ICS 6N 21 / 24

22 What is the basis for R n? One basis (the canonical basis) would be: , ,..., So x 1 b 1 + x 2 b x n b n Xiaohui Xie (UCI) ICS 6N 22 / 24

23 P n : Polynomial functions up to order n f (t) = a 0 + a 1 t a n t n has a basis { 1, t, t 2,..., t n} dim(p n ) = n+1 Xiaohui Xie (UCI) ICS 6N 23 / 24

24 Then any v V v = x 1 b 1 + x 2 b x r b r coordinates of v in terms of the basis [ v ] B = x 1 x 2. x n B = [ b 1 b 2... b r ] Xiaohui Xie (UCI) ICS 6N 24 / 24