Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Office 432 Carver Phone 515-294-8141

Slides:



Advertisements
Similar presentations
Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver Phone
Advertisements

Which augmented matrix represents the following system of equations?
Matrix Algebra Matrix algebra is a means of expressing large numbers of calculations made upon ordered sets of numbers. Often referred to as Linear Algebra.
Matrix Algebra Matrix algebra is a means of expressing large numbers of calculations made upon ordered sets of numbers. Often referred to as Linear Algebra.
1.5 Elementary Matrices and a Method for Finding
1.7 Diagonal, Triangular, and Symmetric Matrices.
Matrices & Systems of Linear Equations
Computer Graphics Recitation 5.
Ch 7.2: Review of Matrices For theoretical and computation reasons, we review results of matrix theory in this section and the next. A matrix A is an m.
Elementary Linear Algebra Anton & Rorres, 9 th Edition Lecture Set – 08 Chapter 8: Linear Transformations.
Finding the Inverse of a Matrix
Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver Phone
3.8 Matrices.
1.7 Diagonal, Triangular, and Symmetric Matrices 1.
Linear Algebra With Applications by Otto Bretscher. Page The Determinant of any diagonal nxn matrix is the product of its diagonal entries. True.
Instructor: Irvin Roy Hentzel Office 432 Carver Phone
259 Lecture 14 Elementary Matrix Theory. 2 Matrix Definition  A matrix is a rectangular array of elements (usually numbers) written in rows and columns.
Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver Phone
Systems of Linear Equation and Matrices
Matrix Algebra. Quick Review Quick Review Solutions.
January 22 Inverse of Matrices. Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver.
Chapter 7 Notes Honors Pre-Calculus. 7.1/7.2 Solving Systems Methods to solve: EXAMPLES: Possible intersections: 1 point, 2 points, none Elimination,
Chapter 2 – Linear Transformations
Elementary Linear Algebra Anton & Rorres, 9th Edition
MAT 2401 Linear Algebra 2.3 The Inverse of a Matrix
Matrix Entry or element Rows, columns Dimensions Matrix Addition/Subtraction Scalar Multiplication.
Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1 : “shiv rpi” Linear Algebra A gentle introduction Linear Algebra has become as basic and as applicable.
2 2.1 © 2016 Pearson Education, Inc. Matrix Algebra MATRIX OPERATIONS.
January 22 Review questions. Math 307 Spring 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver Phone.
Page 146 Chapter 3 True False Questions. 1. The image of a 3x4 matrix is a subspace of R 4 ? False. It is a subspace of R 3.
Sec 3.5 Inverses of Matrices Where A is nxn Finding the inverse of A: Seq or row operations.
Chapter 2 Systems of Linear Equations and Matrices
Matrices. A matrix, A, is a rectangular collection of numbers. A matrix with “m” rows and “n” columns is said to have order m x n. Each entry, or element,
1 C ollege A lgebra Systems and Matrices (Chapter5) 1.
8.3 Another Way of Solving a System of Equations Objectives: 1.) Learn to find the inverse matrix 2.) Use the inverse matrix to a system of equations.
Algebra 3: Section 5.5 Objectives of this Section Find the Sum and Difference of Two Matrices Find Scalar Multiples of a Matrix Find the Product of Two.
Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver Phone
CHAPTER 2 MATRICES 2.1 Operations with Matrices Matrix
Elementary Linear Algebra Anton & Rorres, 9th Edition
Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver Phone
Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Office 432 Carver Phone
Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver Phone
Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver Phone
Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver Phone
For real numbers a and b,we always have ab = ba, which is called the commutative law for multiplication. For matrices, however, AB and BA need not be equal.
2 2.1 © 2012 Pearson Education, Inc. Matrix Algebra MATRIX OPERATIONS.
1.3 Solutions of Linear Systems
TH EDITION LIAL HORNSBY SCHNEIDER COLLEGE ALGEBRA.
Matrices and Determinants
2.5 – Determinants and Multiplicative Inverses of Matrices.
A function is a rule f that associates with each element in a set A one and only one element in a set B. If f associates the element b with the element.
Matrix Algebra Methods for Dummies FIL November Mikkel Wallentin
Chapter 1 Section 1.6 Algebraic Properties of Matrix Operations.
Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver Phone
Linear Algebra Review Tuesday, September 7, 2010.
Boot Camp in Linear Algebra TIM 209 Prof. Ram Akella.
Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver Phone
Linear Algebra by Dr. Shorouk Ossama.
Linear Algebra With Applications by Otto Bretscher.
MTH108 Business Math I Lecture 20.
Matrix Algebra MATRIX OPERATIONS © 2012 Pearson Education, Inc.
Linear Algebra Lecture 2.
Lecture 2 Matrices Lat Time - Course Overview
1.4 Inverses; Rules of Matrix Arithmetic
Finding the Inverse of a Matrix
Matrix Algebra MATRIX OPERATIONS © 2012 Pearson Education, Inc.
Instructor: Irvin Roy Hentzel Office 432 Carver Phone
Sec 3.5 Inverses of Matrices
Linear Algebra A gentle introduction
Matrix Algebra MATRIX OPERATIONS © 2012 Pearson Education, Inc.
Presentation transcript:

Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Office 432 Carver Phone Text: Linear Algebra With Applications, Second Edition Otto Bretscher

Friday, Feb 7 Chapter 2 No hand-in-homework assignment Main Idea: I do not want any surprises on the test. Key Words: Practice test Goal: Test over the material taught in class.

1. The function T|x| = |x-y| is a |y| |y-x| linear transformation. True. It has matrix | 1 -1 |. |-1 1 |

2. Matrix | 1/2 -1/2 | represents a | 1/2 1/2 | rotation. False (1/2) 2 + (1/2) 2 = 1/2 =/= 1

3. If A is any invertible nxn matrix, then rref(A) = In. True. A matrix is invertible if and only if its RCF is the identity.

4. The formula (A 2 ) -1 = (A -1 ) 2 holds for all invertible matrices A. True. A A A -1 A -1 = I.

5. The formula AB=BA holds for all nxn matrices A and B. False. | 0 1| |0 0| =/= | 0 0 | | 0 1 | | 0 0| |1 0| | 1 0 | | 0 0 |

6. If AB = In for two nxn matrices A and B, then A must be the inverse of B. True. This is false if A and B are not square.

7. If A is a 3x4 matrix and B is a 4x5 matrix, then AB will be a 5x3 matrix. False. AB will be a 3x5 matrix.

8. The function T|x| = |y| is a linear |y| |1| transformation. False. T (2 |0|) = |0| =/= 2 T|0| = | 0 | |0| |1| |0| | 2 |

9. The matrix | 5 6 | represents a |-6 5 | rotation-dilation. True. The dilation is by Sqrt[61] the angle is ArcTan[-6/5] = radians

10. If A is any invertible nxn matrix, then A commutes with A -1. True. By definition, A A -1 = A -1 A = I

11. Matrix | 1 2 | is invertible. | 3 6 | False. The RCF is | 1 2 |. | 0 0 |

| | Matrix | | is invertible. | | True. | | | 1 0 1| | | | | ~ | 0 1 0| ~ | | | | | | | |

13. There is an upper triangular 2x2 matrix A such that A 2 = | 1 1 | | 0 1 | True. A = | 1 1/2 | is one possibility. | 0 1 |

14. The function T|x| = |(y+1) 2 – (y-1) 2 | is a linear |y| |(x-3) 2 – (x+3) 2 | transformation. True. T|x| = | 4 y|. |y| |-12 x|

15. Matrix | k -2 | is invertible for all | 5 k-6 | real numbers k. True. | k -2 | ~ | 1 (k-6)/5 | ~ | 1 (k-6)/5 | | 5 k-6 | | k -2 | | 0 (-k^2+6k-10)/5| This polynomial has roots 3 (+/-) i so for all REAL numbers k, the RCF is I and it is invertible.

16. There is a real number k such that the matrix | k-1 -2 | fails to be invertible. | -4 k-3 | True. k = -1 | | k = 5 | 4 -2 |. | | | -4 2 |

17. There is a real number k such that the matrix | k-2 3 | fails to be | -3 k-2 | invertible. False. | k-2 3 | ~ | 1 -(k-2)/3 | ~ | 1 -(k-2)/3 | | -3 k-2 | | k-2 3 | | 0 (k-2) 2 +3| the roots are k = 2 (+/-) i Sqrt[3] which are not real.

18. Matrix | | represents a | | rotation. True: theta = Pi + ArcCos[0.6] =

19. The formula det(2A) = 2 det(A) holds for all 2x2 matrices A. False. det(2A) = 4 det(A).

20. There is a matrix A such that | 1 2 | A | 5 6 | = | 1 1 |. | 3 4 | | 7 8 | | 1 1 | True | 1 2 | -1 | 1 1 | | 5 6 | |-1 | 3 4 | | 1 1 | | 7 8 | Should work. 1/2 | 1 -1 | | -1 1 |

21. There is a matrix A such that A | 1 1 | = | 1 2 |. | 1 1 | | 1 2 | False Any linear combination of the rows of | 1 1 | will look like | x x |. | 1 1 | | y y |

22. There is a matrix A such that | 1 2 | A = | 1 1 |, | 1 2 | | 1 1 | True. | 1 1 | works. | 0 0 |

23. Matrix | -1 2 | represents a shear. | -2 3 | False | -1 2 | |x| = | -x + 2y| = |x| +2(-x+y) | 1| | -2 3 | |y| | -2x+3y| |y| | 1| The fixed vector has | 1 |. | 1 |

24. | 1 k | 3 = | 1 3k | for all real | 0 1 | | 0 1 | numbers k. True:

25. The matrix product | a b | | d -b | is always a scalar | c d | | -c a | of I 2. True. The scalar is ad-bc.

26. There is a nonzero upper triangular 2x2 matrix A such that A 2 = | 0 0 |. | 0 0 | True. A = | 0 1 | is one possibility. | 0 0 |

27. There is a positive integer n such that | 0 -1 | n = I 2. | 1 0 | True. n = 4 is one possibility.

28. There is an invertible 2x2 matrix A such that A -1 = | 1 1 |. | 1 1 | False. The RCF of | 1 1 | = | 1 1 | | 1 1 | | 0 0 | so | 1 1 | cannot be an invertible matrix. | 1 1 |

29. There is an invertible nxn matrix with two identical rows. False. If A has two identical rows, then AB has 2 identical rows also. Thus AB cannot be I.

30. If A 2 = I n, then matrix A must be invertible. True. In fact, A is its own inverse.

31. If A 17 = I 2, then A must be I 2. False A = | Cos[t] -Sin[t] | | Sin[t] Cos[t] | Where t = 2 Pi/17 should work.

32. If A 2 = I 2, then A must be either I 2 or –I 2. False A = | -1 0 | is one possibility. | 0 1 |

33. If matrix A is invertible, then matrix 5 A is invertible as well. True. And (5A) -1 = 1/5 A -1.

34. If A and B are two 4x3 matrices such that AV = BV for all vectors v in R 3, then matrices A and B must be equal. True. It follows that AI = BI for the 3x3 identity matrix I. Thus A=B.

35. If matrices A and B commute, then the formula A 2 B = BA 2 must hold. True. A 2 B = AAB = ABA=BAA=BA 2.

36. If A 2 = A for an invertible nxn matrix A, then A must be I n. True. Multiply through by A -1 giving A=I.

37. If matrices A and B are both invertible, then matrix A+B must be invertible as well. False. Let B = -A.

38. The equation A 2 = A holds for all 2x2 matrices A representing an orthogonal projection. True. Once you have projected once by A, subequent actions by A will simply fix the vector.

39. If matrix | a b c | is invertible, then | d e f | | g h I | matrix | a b | must be invertible as well. | d e | | | False. | | Is an example. | |

40. If A 2 is invertible, then matrix A itself must be invertible. True. For A 2 to be defined, then A must be square. If AAB = I, then A must be right invertible so A is invertible.

41. The equation A -1 = A holds for all 2x2 matrices A representing a reflection. True. For a reflection A 2 = I.

42. The formula (AV).(AW) = V.W holds for all invertible 2x2 matrices A and for all vectors V and W in R 2. False. | 1 1 | | 0 |.| 1 1 | | 1 | = 1 | 0 1 | | 1 | | 0 1| | 0 |

43. There exist a 2x3 matrix A and a 3x2 matrix B such that AB = I 3. True. | | | 1 0 | = | 1 0 | | | | 0 1| | 0 1 | | 0 0|

44. There exist a 3x2 matrix A and a 2x3 matrix B such that AB = I 3. False. There must be some X =/= 0 such that BX = 0. Then 0 = ABX = X. Contradiction.

45. If A 2 + 3A + 4 I 3 = 0 for a 3x3 matrix A then A must be invertible. True. A(A+3) = -4 I 3 so the inverse of A is (-1/4)(A+3).

46. If A is an nxn such that A 2 = 0, then matrix I n +A must be invertible. True. (I n +A)(I n -A) = I.

47. If matrix A represents a shear, then the formula A 2 -2A+I 2 = 0 must hold. True. (A-I)X will be a fixed vector. So A(A-I)X = (A-I)X which means A2-2A+I = 0.

48. If T is any linear transformation from R 3 to R 3, then T(VxW) = T(V)xT(W) for all vectors V and W in R 3. | | | 1 | | 0 | False. T = | | V = | 0 | W = | 0 | | | | 0 | | 1 | | 0 | | 0 | | 1 | | 1 | | 0 | T[VxW] = T| -1 | = |-1 | (TV)x(TW) = | 0 | x| 1 } = | -1 |. | 0 | | 0 | | 0 | } 1 } | 1 |

49. There is an invertible 10x10 matrix that has 92 ones among its entries. False. There are only 8 entries which are not one. At least 2 columns have only ones. Matrices with 2 identical columns are not invertible.

50. The formula rref(AB) = rref(A)rref(B) holds for all mxn matrices A and for all nxp matrices B. False A = B = | 0 0 | | 1 0 | rref(AB) =| 0 0 | rref(A)rref(B) = | 1 0 | | 0 0 | | 0 0 |