1. 4-1 4.1 Vectors in R n a sequence of n real number An ordered n-tuple: the set of all ordered n-tuple  n-space: R n Notes: (1) An n-tuple can be viewed as a point in R n with the x i ’s as its coordinates. (2) An n-tuple can be viewed as a vector in R n with the x i ’s as its components. Chapter 4 Vector Spaces

2. 4-2 n = 4 = set of all ordered quadruple of real numbers R 4 = 4-space R 1 = 1-space = set of all real number n = 1 n = 2 R 2 = 2-space = set of all ordered pair of real numbers n = 3 R 3 = 3-space = set of all ordered triple of real numbers Ex: a pointa vector

3. 4-3  Equal: if and only if  Vector addition (the sum of u and v):  Scalar multiplication (the scalar multiple of u by c):  Notes: The sum of two vectors and the scalar multiple of a vector in R n are called the standard operations in R n. (two vectors in R n )

4. 4-4  Negative:  Difference:  Zero vector:  Notes: (1) The zero vector 0 in R n is called the additive identity in R n. (2) The vector –v is called the additive inverse of v.

5. 4-5

6. 4-6

7. 4-7  Notes: A vector in can be viewed as: or a n×1 column matrix (column vector): a 1×n row matrix (row vector):

8. 4-8 Vector additionScalar multiplication The matrix operations of addition and scalar multiplication give the same results as the corresponding vector representations

9. 4-9 4.2 Vector Spaces Notes:A vector space consists of four entities: a set of vectors, a set of scalars, and two operations

10. 4-10 Examples of vector spaces: (1) n-tuple space: R n (2) Matrix space: (the set of all m×n matrices with real values) Ex: ： (m = n = 2) vector addition scalar multiplication vector addition scalar multiplication

11. 4-11 (3) n-th degree polynomial space: (the set of all real polynomials of degree n or less) (4) Function space : ( the set of all real-valued continuous functions defined on the entire real line.)

12. 4-12

13. 4-13 Notes: To show that a set is not a vector space, you need only to find one axiom that is not satisfied.

14. 4-14 4.3 Subspaces of Vector Space  Trivial subspace: Every vector space V has at least two subspaces. (1) Zero vector space {0} is a subspace of V. (2) V is a subspace of V.  Definition of Subspace of a Vector Space: A nonempty subset W of a vector space V is called a subspace of V if W is itself a vector space under the operations of addition and scalar multiplication defined in V.

15. 4-15

16. 4-16 4.4 Spanning Sets and Linear Independence

17. 4-17

18. 4-18  Notes:

19. 4-19  Notes:

20. 4-20 Note:

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23. 4-23

24. 4-24 4.5 Basis and dimension  Notes: (1) the standard basis for R 3 : {i, j, k} i = (1, 0, 0), j = (0, 1, 0), k = (0, 0, 1) (2) the standard basis for R n : {e 1, e 2, …, e n } e 1 =(1,0,…,0), e 2 =(0,1,…,0), e n =(0,0,…,1) Ex: R 4 {(1,0,0,0), (0,1,0,0), (0,0,1,0), (0,0,0,1)}

25. 4-25 Ex: matrix space: (3) the standard basis for m  n matrix space: { E ij | 1  i  m, 1  j  n } (4) the standard basis for polynomials P n (x): {1, x, x 2, …, x n } Ex: P 3 (x) {1, x, x 2, x 3 }

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27. 4-27

28. 4-28 Ex: (1) Vector space R n  basis {e 1, e 2, , e n } (2) Vector space M m  n  basis {E ij | 1  i  m, 1  j  n} (3) Vector space P n (x)  basis {1, x, x 2, , x n } (4) Vector space P(x)  basis {1, x, x 2,  }  dim(R n ) = n  dim(M m  n )=mn  dim(P n (x)) = n+1  dim(P(x)) = 

29. 4-29

30. 4-30 4.6 Rank of a Matrix and System of Linear Equations Row vectors of A  row vectors: Column vectors of A  column vectors: || || || A (1) A (2) A (n)

31. 4-31 Notes: (1) The row space of a matrix is not changed by elementary row operations. (2) Elementary row operations can change the column space.

32. 4-32

33. 4-33  Notes: rank(A T ) = rank(A) Pf: rank(A T ) = dim(RS(A T )) = dim(CS(A)) = rank(A)

34. 4-34  Notes: (1) The nullspace of A is also called the solution space of the homogeneous system Ax = 0. (2) nullity(A) = dim(NS(A))

35. 4-35 Notes: (1) rank(A): The number of leading variables in the solution of Ax=0. (The number of nonzero rows in the row-echelon form of A) (2) nullity (A): The number of free variables in the solution of Ax = 0.

36. 4-36 Fundamental SpaceDimension RS(A)=CS(A T )r CS(A)=RS(A T )r NS(A)n – r NS(A T )m – r Notes: If A is an m  n matrix and rank(A) = r, then

37. 4-37

38. 4-38  Notes: If rank([A|b])=rank(A), then the system Ax=b is consistent.

39. 4-39

40. 4-40 4.7 Coordinates and Change of Basis

41. 4-41 Change of basis problem: Given the coordinates of a vector relative to one basis B and want to find the coordinates relative to another basis B'.

42. 4-42  Ex: (Change of basis) Consider two bases for a vector space V Let

43. 4-43 Transition matrix from B' to B: where is called the transition matrix from B' to B If [v] B is the coordinate matrix of v relative to B [v] B‘ is the coordinate matrix of v relative to B'

44. 4-44

45. 4-45 Notes:

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47. 4-47 4.8 Applications of Vector Spaces

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