Midterm 1 Solutions, MATH 54, Linear Algebra and Differential Equations, Fall Problem Maximum Score Your Score

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1 Midterm Solutions, MATH 54, Linear Algebra and Differential Equations, Fall 24 Student ID: Circle your section: 2 Shin 8am 7 Evans 22 Lim pm 35 Etcheverry 22 Cho 8am 75 Evans 23 Tanzer 2pm 35 Evans 23 Shin 9am 5 Latimer 24 Moody 2pm 8 Evans 24 Cho 9am 254 Sutardja Dai 25 Tanzer 3pm 26 Wheeler 25 Zhou am 254 Sutardja Dai 26 Moody 3pm 6 Evans 26 Theerakarn am 79 Stanley 27 Lim 8am 3 Hearst 27 Theerakarn am 79 Stanley 28 Moody 5pm 7 Evans 28 Zhou am 254 Sutardja Dai 29 Lee 5pm 3 Etcheverry 29 Wong 2pm 3 Evans 22 Williams 2pm 289 Cory 2 Tabrizian 2pm 9 Evans 22 Williams 3pm 4 Barrows 2 Wong pm 254 Sutardja Dai 222 Williams 2pm 22 Wheeler If none of the above, please explain: This is a closed book exam, no notes allowed. It consists of 6 problems, each worth points, of which you must complete 5. Choose one problem not to be graded by crossing it out in the box below. If you forget to cross out a problem, we will roll a die to choose one for you. Problem Maximum Score Your Score Total Possible 5

2 Midterm Solutions, MATH 54, Linear Algebra and Differential Equations, Fall 24 Problem ) Decide if the following statements are ALWAYS TRUE or SOMETIMES FALSE. You do not need to justify your answers. Enter your answers of T or F in the boxes of the chart. (Correct answers receive 2 points, incorrect answers -2 points, blank answers points.) Statement Answer T F T F F ) If a linear transformation T : R n R m is given by a matrix A, then the range of T is equal to the column space of A. Both subspaces are the span of the columns of A. 2) If two matrices have equal reduced row echelon forms, then their column spaces are equal. Counterexample: [ ] [ ] 3) If a finite set of vectors spans a vector space, then some subset of the vectors is a basis. If the set is linearly independent, then it is a basis. If not, some vector is a linear combination of the others. Throw out that vector and check that remaining set still spans. Repeat until set is linearly independent. 4) If A is a 2 2 matrix such that A 2 =, then A =. Counterexample: A = [ ] 5) If A is a 5 5 matrix such that det(2a) = 2 det(a), then A =. Counterexample: 2

3 Midterm Solutions, MATH 54, Linear Algebra and Differential Equations, Fall 24 Problem 2) Indicate with an X in the chart all of the answers that satisfy the questions below. You do not need to justify your answers. It is possible that any number of the answers (including possibly none) satisfy the questions. (A completely correct row of the chart receives 2 points, a partially correct row receives point, but any incorrect X in a row leads to points.) (a) (b) (c) (d) (e) Question X Question 2 X X Question 3 X X X Question 4 X X X Question 5 X X X Inside of R 3, consider the vectors v = v 2 = v 3 = v 4 = v 5 = v 6 = ) Which of the following lists are linearly independent? a) v, v 2. b) v 2, v 3, v 4. c) v, v 3, v 5. d) v 2, v 4, v 6. e) v 3, v 4, v 5, v 6. 2) Which of the following lists span R 3? a) v, v 2. b) v 2, v 3, v 4. c) v, v 3, v 5. d) v 2, v 4, v 6. e) v 3, v 4, v 5, v 6. 3

4 Midterm Solutions, MATH 54, Linear Algebra and Differential Equations, Fall 24 3) Which of the following matrices have reduced row echelon form equal to? a) b) c) d) e) 2 2 4) Inside of R 3, consider the subset of vectors {v = satisfying the following requirements. Which of them are subspaces? a b a } a) a and b are both zero. b) a is any number and b is zero. c) a is zero or b is zero or both are zero. d) a and b are equal. e) a, b are both positive, both negative, or both zero. 5) Suppose T : R 3 R 3 has 2-dimensional range and we know T ( ) = 2 T ( ) = Which of the following are a possible standard matrix of T? a) 2 2 b) c) 2 2 d) 2 2 e)

5 Midterm Solutions, MATH 54, Linear Algebra and Differential Equations, Fall 24 Problem 3) For a real number c, consider the linear system x + x 2 + cx 3 + x 4 = c x 2 + x 3 + 2x 4 = x + 2x 2 + x 3 x 4 = c a) (5 points) For what c, does the linear system have a solution? Let us find the REF of the augmented matrix c c c c c c c c 2 2c 2 c 2c Thus the linear system has a solution if and only if c 2. b) (5 points) Find a basis of the subspace of solutions when c =. When c =, the REF of the unaugmented matrix is 2 2 The free variable is x 4 and so solutions are of the form 3x 4 2x 4 x 4 Thus a basis consists of the single vector 3 2 5

6 Midterm Solutions, MATH 54, Linear Algebra and Differential Equations, Fall 24 Problem 4) ( points) Let P 2 be the vector space of polynomials of degree less than or equal to 2. Let B be the basis b = x 2, b 2 = + x, b 3 = x + x 2. Find the coordinates of the vector v = + 2x x 2 with respect to B. Writing all polynomials in terms of the standard basis, x, x 2, we find we must solve the linear system with augmented matrix 2 Let us put it into REF Now we find the solution which is the sought-after coordinate vector 4 [v] B = 3 6

7 Midterm Solutions, MATH 54, Linear Algebra and Differential Equations, Fall 24 Problem 5) Consider the matrices A = 2 2 B = 2 2 ) (5 points) Calculate the matrix AB. AB = ) (5 points) Calculate the determinant det(ab). Cite any methods used in your answer. det(ab) = since AB is not invertible. The reason AB is not invertible is AB has a nontrivial null space. The reason AB has a nontrivial null space is that B maps R 4 to R 3 and so B must have a nontrivial null space, and AB results from first applying B then A, so any vector in the null space of B will be in the null space of AB. 7

8 Midterm Solutions, MATH 54, Linear Algebra and Differential Equations, Fall 24 Problem 6) ) (6 points) Fill in the blanks (each worth /2 a point) in the proof of the following assertion. Assertion. If A is a square matrix, and the linear transformation x Ax is injective, then the linear transformation x A T x is injective. Proof. For any m n matrix A, recall that and similarly for A T, we have We also know for A and A T that n = rank(a) + dim N ul(a) m = rank(a T ) + dim N ul(a T ) rank(a) = rank(a T ) Next recall that x Ax is injective if and only if dim N ul(a) = and similarly, x A T x is injective if and only if dim N ul(a T ) = Thus when A is square, so m = n, and x Ax is injective, we have rank(a) = n = m = rank(a T ) + dim N ul(a T ) And so we conclude that and hence x A T x is injective. dim N ul(a T ) = 2) (4 points) Give an example of a 2 2 matrix A such that N ul(a) N ul(a T ). [ ] [ ] A = N ul(a) = Span{ } [ ] [ ] A T = N ul(a T ) = Span{ } 8