1 / 52

Linear Algebra Basics: Basis, Dimension, and Rank

Learn about spanning sets, linear independence, row/column spaces, basis, rank of matrices, systems of linear equations, and more in elementary linear algebra. Understand dot product, coordinates, and change of basis in vector spaces.

lfortner
Download Presentation

Linear Algebra Basics: Basis, Dimension, and Rank

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Lecture 9 Vector & Inner Product Spaces Last Time Spanning Sets and Linear Independence (Cont.) Basis and Dimension Rank of a Matrix and Systems of Linear Equations Elementary Linear Algebra R. Larsen et al. (6th Edition) TKUEE翁慶昌-NTUEE SCC_12_2008

  2. Lecture 9: Vector Space (cont.) Today Rank of a Matrix and Systems of Linear Equations (Cont.) Coordinates and Change of Basis Applications of Vector Space Length and Dot Product in Rn Reading Assignment: Secs 5.2-5.5 9 - 2

  3. 4.6 Rank of a Matrix and Systems of Linear Equations • row vectors: Row vectors of A • column vectors: Column vectors of A || || || A(1)A(2)A(n) 9 - 3

  4. Column space: • The column space of A is the subspace of Rm spanned by • the column vectors of A. • Null space: The null space of A is the set of all solutions of Ax=0 and it is a subspace of Rn. Let A be an m×n matrix. • Row space: The row space of A is the subspace of Rn spanned by the row vectors of A.

  5. Notes: (1) The row space of a matrix is not changed by elementary row operations. RS(r(A)) = RS(A) r: elementary row operations (2) Elementary row operations can change the column space. • Thm 4.13: (Row-equivalent matrices have the same row space) If an mn matrix A is row equivalent to an mn matrix B, then the row space of A is equal to the row space of B.

  6. Thm 4.14: (Basis for the row space of a matrix) • If a matrix A is row equivalent to a matrix B in row-echelon • form, then the nonzero row vectors of B form a basis for the • row space of A.

  7. Sol: B = A= • Ex 2: ( Finding a basis for a row space) Find a basis of row space of A =

  8. Notes: a basis for RS(A) = {the nonzero row vectors of B} (Thm 4.14) = {w1, w2, w3} = {(1, 3, 1, 3) , (0, 1, 1, 0) ,(0, 0, 0, 1)}

  9. Ex 3: (Finding a basis for a subspace) Find a basis for the subspace of R3 spanned by A = G.E. Sol: a basis for span({v1, v2, v3}) = a basis forRS(A) = {the nonzero row vectors of B} (Thm 4.14) = {w1, w2} = {(1, –2, – 5) , (0, 1, 3)}

  10. Ex 4: (Finding a basis for the column space of a matrix) Find a basis for the column space of the matrix A given in Ex 2. Sol. 1:

  11. CS(A)=RS(AT) a basis for CS(A) = a basis for RS(AT) = {the nonzero vectors of B} = {w1, w2, w3} (a basis for the column space of A) • Note: This basis is not a subset of {c1, c2, c3, c4}.

  12. Sol. 2: Leading 1 => {v1, v2, v4} is a basis for CS(B) {c1, c2, c4} is a basis for CS(A) • Notes: (1) This basis is a subset of {c1, c2, c3, c4}. (2) v3 = –2v1+ v2, thus c3 = – 2c1+ c2 .

  13. Thm 4.16: (Solutions of a homogeneous system) If A is anmn matrix, then the set of all solutions of the homogeneous system of linear equations Ax = 0 is a subspace of Rn called the nullspace of A. Pf: • Notes: The nullspace of A is also called the solution space of the homogeneous system Ax = 0.

  14. Ex 6: (Finding the solution space of a homogeneous system) Find the nullspace of the matrix A. Sol: The nullspace of A is the solution space of Ax = 0. x1 = –2s – 3t, x2 = s, x3 = –t, x4 = t

  15. Thm 4.15: (Row and column space have equal dimensions) If A is anmn matrix, then the row space and the column space of A have the same dimension. dim(RS(A)) = dim(CS(A)) • Rank: The dimension of the row (or column) space of a matrix A is called the rank of A. rank(A) = dim(RS(A)) = dim(CS(A))

  16. Pf: rank(AT) = dim(RS(AT)) = dim(CS(A)) = rank(A) • Nullity: • The dimension of the nullspace of A is called the nullity of A. • nullity(A) = dim(NS(A)) • Notes:rank(AT) = rank(A)

  17. 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. • Thm 4.17: (Dimension of the solution space) If A is an mn matrix of rank r, then the dimension of the solution space of Ax = 0 is n – r. That is n = rank(A) + nullity(A)

  18. Notes: If A is an mn matrix and rank(A) = r, then

  19. Ex 7: (Rank and nullity of a matrix) Let the column vectors of the matrix A be denoted by a1, a2, a3, a4, and a5. a1a2a3 a4a5 (a) Find the rank and nullity of A. (b) Find a subset of the column vectors of A that forms a basis for the column space of A . (c) If possible, write the third column of A as a linear combination of the first two columns.

  20. a1a2 a3a4a5b1b2b3b4b5 Sol: Let B be the reduced row-echelon form of A. (a) rank(A) = 3 (the number of nonzero rows in B)

  21. (c) (b) Leading 1

  22. Thm 4.18: (Solutions of a nonhomogeneous linear system) If xp is a particular solution of the nonhomogeneous system Ax = b, then every solution of this system can be written in the formx = xp + xh , wher xh is a solution of the corresponding homogeneous system Ax = 0. is a solution of Ax = 0 Pf: Let x be any solution of Ax = b.

  23. Ex 8: (Finding the solution set of a nonhomogeneous system) Find the set of all solution vectors of the system of linear equations. Sol: s t

  24. i.e. is a particular solution vector of Ax=b. xh= su1 + tu2 is a solution of Ax = 0

  25. Thm 4.19: (Solution of a system of linear equations) The system of linear equations Ax = b is consistent if and only if b is in the column space ofA. Pf: Let be the coefficient matrix, the column matrix of unknowns, and the right-hand side, respectively, of the system Ax = b.

  26. Then Hence, Ax = b is consistent if and only if b is a linear combination of the columns of A. That is, the system is consistent if and only if b is in the subspace of Rm spanned by the columns of A.

  27. Ex 9:(Consistency of a system of linear equations) • Notes: If rank([A|b])=rank(A) Then the system Ax=b is consistent. Sol:

  28. c1 c2 c3 bw1 w2 w3 v (b is in the column space of A) The system of linear equations is consistent. • Check:

  29. Summary of equivalent conditions for square matrices: If A is an n×n matrix, then the following conditions are equivalent. (1) A is invertible (2) Ax = b has a unique solution for any n×1 matrix b. (3) Ax = 0 has only the trivial solution (4) A is row-equivalent to In (5) (6) rank(A) = n (7) The n row vectors of A are linearly independent. (8) The n column vectors of A are linearly independent.

  30. Keywords in Section 4.6: row space : 列空間 column space : 行空間 null space: 零空間 solution space : 解空間 rank: 秩 nullity : 核次數

  31. Lecture 9: Vector Space (cont.) Today Rank of a Matrix and Systems of Linear Equations (Cont.) Coordinates and Change of Basis Applications of Vector Space Length and Dot Product inRn

  32. Applications Null Space and Feasible Search Matrix Rank and Nullity.doc Industry Robot

  33. 4.7 Coordinates and Change of Basis Coordinate representation relative to a basis Let B = {v1, v2, …, vn} be an ordered basis for a vector space V and let x be a vector in V such that The scalars c1, c2, …, cn are called the coordinates of x relative to the basis B. The coordinate matrix (or coordinate vector) ofx relative to B is the column matrix in Rn whose components are the coordinates of x.

  34. Ex 1: (Coordinates and components in Rn) Find the coordinate matrix of x = (–2, 1, 3) in R3 relative to the standard basisS = {(1, 0, 0), ( 0, 1, 0), (0, 0, 1)} Sol:

  35. Ex 3: (Finding a coordinate matrix relative to a nonstandard basis) Find the coordinate matrix of x=(1, 2, –1) in R3 relative to the (nonstandard) basisB ' = {u1, u2, u3}={(1, 0, 1), (0, – 1, 2), (2, 3, – 5)} Sol:

  36. Change of basis problem: You were given the coordinates of a vector relative to one basis B and were asked to find the coordinates relative to another basis B'. • Ex: (Change of basis) • Consider two bases for a vector space V

  37. Let

  38. 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' where is called the transition matrix from B' to B

  39. Thm 4.20: (The inverse of a transition matrix) If P is the transition matrix from a basis B'to a basis B in Rn, then (1) P is invertible (2) The transition matrix from B to B' is P–1 • Notes:

  40. Thm 4.21: (Transition matrix from B to B') Let B={v1, v2, … , vn} and B'={u1, u2,… , un} be two bases for Rn. Then the transition matrix P–1 from B to B' can be found by using Gauss-Jordan elimination on the n×2n matrix as follows.

  41. Ex 5: (Finding a transition matrix) B={(–3, 2), (4,–2)} and B' ={(–1, 2), (2,–2)} are two bases for R2 (a) Find the transition matrix from B' to B. (b) (c) Find the transition matrix from B to B' .

  42. Sol: (a) G.J.E. BB' IP-1 [I2 : P] = (the transition matrix from B' to B) (b)

  43. (c) G.J.E. • Check: B'B IP-1 (the transition matrix from B toB')

  44. Rotation of the Coordinate Axes

  45. Ex 6: (Coordinate representation in P3(x)) Find the coordinate matrix of p = 3x3-2x2+4 relative to the standard basis in P3(x), S = {1, 1+x, 1+ x2, 1+ x3}. Sol: p = 3(1) + 0(1+x) + (–2)(1+x2 ) + 3(1+x3) [p]s =

  46. Ex: (Coordinate representation in M2x2) Find the coordinate matrix of x = relative to the standardbasis in M2x2. B = Sol:

  47. Keywords in Section 4.7: coordinates of x relative to B：x相對於B的座標 coordinate matrix：座標矩陣 coordinate vector：座標向量 change of basis problem：基底變換問題 transition matrix from B' to B：從 B'到 B的轉移矩陣

More Related