Math 113 Practice Final Solutions
|
|
- Paul Wilkinson
- 5 years ago
- Views:
Transcription
1 Math 113 Practice Final Solutions 1
2 There are 9 problems; attempt all of them. Problem 9 (i) is a regular problem, but 9(ii)-(iii) are bonus problems, and they are not part of your regular score. So do them only if you have completed all other problems, as well as 9(i). This is a closed book, closed notes exam, with no calculators allowed (they shouldn t be useful anyway). You may use any theorem, proposition, etc., proved in class or in the book, provided that you quote it precisely, and provided you are not asked explicitly to prove it. Make sure that you justify your answer to each question, including the verification that all assumptions of any theorem you quote hold. Try to be brief though. If on a problem you cannot do part (i) or (ii), you may assume its result for the subsequent parts. All unspecified vector spaces can be assumed finite dimensional unless stated otherwise. Allotted time: 3 hours. Total regular score: 16 points. Problem 1. (2 points) (i) Suppose V is finite dimensional, S, T L(V ). Show that ST = I if and only if TS = I. (ii) Suppose V, W are finite dimensional, S L(W, V ), T L(V, W ), ST = I. Does this imply that TS = I? Prove this, or give a counterexample. (iii) Suppose that S, T L(V ) and ST is nilpotent. Show that TS is nilpotent. Solution 1. (i) We prove ST = I implies TS = I; the converse follows by reversing the role of S and T. So suppose ST = I. Then S is surjective (since for v V, v = S(Tv)) and T is injective (for Tv = implies = S(Tv) = (ST)v = v), so as V is finite dimensional, they are both invertible. Thus, S has an inverse R, so RS = I in particular. But T = IT =(RS)T = R(ST)=RI = R, so TS = I as claimed. (ii) Suppose V = {}, W = F, T = (V has only one element!), Sw = for all w W. Then ST =, so ST = I, but T Sw = for all w W, and as W is not the trivial vector space, TS I. (While this is the lowest dimensional possibility, with dim V =, dim W = 1, it is easy to add dimensions: e.g. V = F, W = F 2, Tv =(v, ), S(w 1,w 2 )=w 1, so ST v = v for v F, TS(w 1,w 2 ) = (w 1, ), which is not the identity map.) (iii) Suppose (ST) k =. Then (TS) k+1 = T (ST) k S = T S =. Problem 2. (15 points) Suppose that T L(V, W ) is injective and (v 1,...,v n ) is linearly independent in V. Show that (Tv 1,..., T v n ) is linearly independent in W. Solution 2. Suppose that n a jtv j =. Then T ( n a jv j )= n a jtv j =. As T is injective, this implies n a jv j =. Since (v 1,...,v n ) is linearly independent in V, all a j are. Thus, (Tv 1,..., T v n ) is linearly independent in W. Problem 3. (2 points) Consider the following map (.,.) :R 2 R 2 R: x, y =(x 1 + x 2 )(y 1 + y 2 ) + (2x 1 + x 2 )(2y 1 + y 2 ); x =(x 1,x 2 ), y =(y 1,y 2 ). 1
3 2 MATH 113: PRACTICE FINAL SOLUTIONS (i) Show that.,. is an inner product on R 2. (ii) Find an orthonormal basis of R 2 with respect to this inner product. (iii) Let φ(x) =x 1, x =(x 1,x 2 ). Find y R 2 such that φ(x) = x, y for all x R 2. Solution 3. (i) x, x =(x 1 + x 2 ) 2 + (2x 1 + x 2 ) 2, and is equal to if and only if x 1 = x 2 and 2x 1 = x 2, so x 1 = x 2 =. Thus,.,. is positive definite. As x, y are symmetric in the definition, it is clearly symmetric, and ax, y =(ax 1 + ax 2 )(y 1 + y 2 ) + (2ax 1 + ax 2 )(2y 1 + y 2 ) = a((x 1 + x 2 )(y 1 + y 2 ) + (2x 1 + x 2 )(2y 1 + y 2 )) = a x, y, with a similar argument for x + x,y = x, y + x,y. (ii) One can apply Gram-Schmidt to the basis ((1, ), (, 1)). As (1, ) 2 = = 5, e 1 = 1 5 (1, ). Then (1, ), (, 1) = = 3, so (, 1) (, 1),e 1 e 1 = (, 1) 3 5 (1, ) = ( 3 5, 1). Normalizing: ( 3 5, 1) 2 =( 2 5 )2 +( 1 5 )2 = 1 5, so e 2 = 5( 3 5, 1). Then (e 1,e 2 ) is an orthonormal basis. (iii) If (e 1,e 2 ) is an orthonormal basis, then y = φ(e 1 )e 1 + φ(e 2 )e 2. Using the basis from the previous part gives y = 1 5 e 1 + 5( 3 5 )e 2 = (2, 3). We check that (x 1,x 2 ), (2, 3) =(x 1 + x 2 )( 1) + (2x 1 + x 2 ) 1=x 1. Problem 4. (15 points) Suppose V is a complex inner product space. Let (e 1,e 2,...,e n ) be an orthonormal basis of V. Show that trace(t T )= Te j 2. Solution 4. If A L(V ), then the matrix entries of A with respect to the basis (e 1,...,e n ) are Ae j,e i, since as (e 1,...,e n ) is orthonormal. Thus, Ae j = Ae j,e 1 e Ae j,e n e n trace A = trace M(A, (e 1,...,e n )) = Applying this with A = T T : trace T = T Te j,e j = Ae j,e j. Te j, T e j = Te j 2. Problem 5. (2 points) Find p P 3 (R) such that p() =, p () =, and is as small as possible. 1 p(x) 2 dx
4 MATH 113: PRACTICE FINAL SOLUTIONS 3 Solution 5. Let U be the subspace of P 3 (R) consisting of polynomials p with p() = and p () =. These are exactly the polynomials ax 2 + bx 3, a, b R. Let p, q be the inner product p, q = p(x) q(x) dx on P 3(R). As the constant function 1 P 3 (R), the p U that minimizes 1 p 2 (which is what we want) is the orthogonal projection P U 1 of 1 onto U, so we just need to find this. There are different ways one can proceed. One can find an orthonormal basis (e 1,e 2 ) of U using Gram-Schmidt applied to the basis (x 2,x 3 ), and then P U 1= 1,e 1 e 1 + 1,e 2 e 2. Explicitly, as (x2 ) 2 dx = 1 5, e 1 = 5x 2, then as x3 x 2 dx = 1 6, and finally x 3 x 3,e 1 e 1 = x x2, (x x2 ) 2 dx = , one has e 2 = 7 6 (x x2 ). Thus, using we deduce that 1 x 2 dx = 1 3, 1 (x x2 ) dx = = 1 36, P U 1= 5 3 x2 7(x x2 ). A different way of proceeding is to work without orthonormal bases, rather use the characterization of P U 1 as the unique element of U with 1 P U 1 orthogonal to all vectors in U. So suppose P U 1=ax 2 +bx 3. Then we must have 1 P U 1,x 2 = and 1 P U 1,x 3 = which give = = (1 ax 2 bx 3 )x 2 dx = 1 3 a 5 b 6, (1 ax 2 bx 3 )x 3 dx = 1 4 a 6 b 7, so 21 1 = 6a +5b, =7a +6b, 2 taking 7 times the first minus 6 times the second gives 7 63 = b, so b = 7, while taking 6 times the first minus 5 times the second gives = a, 2 so a = = Problem 6. (2 points) Suppose that S, T L(V ), S invertible. (i) Prove that if p P(F) is a polynomial then p(st S 1 )=Sp(T )S 1. (ii) Show that the generalized eigenspaces of ST S 1 are given by null(st S 1 λi) dim V = {Sv : v null(t λi) dim V }. (iii) Show that trace(st S 1 ) = trace(t ), where trace is the operator trace (sum of eigenvalues, with multiplicity). (Assume that F = C in this part.)
5 4 MATH 113: PRACTICE FINAL SOLUTIONS Solution 6. (i) Observe that (ST S 1 ) k = ST k S 1 for all non-negative integers k. Indeed, this is immediate for k =, 1. In general, it is easy to prove this by induction: if it holds for some k, then (ST S 1 ) k+1 =(ST S 1 ) k ST S 1 = ST k S 1 ST S 1 = ST k+1 S 1, providing the inductive step and proving the claim. Now, if p P(F) then p = m j= a jz j, a j F, and m m m p(st S 1 )= a j (ST S 1 ) j = a j ST j S 1 = S( a j T j )S 1 = Sp(T )S 1 j= as claimed. (ii) We first show j= j= {Sv : v null(t λi) dim V } null(st S 1 λi) dim V. Indeed, let p(z) = (z λ) dim V. If v null(t λi) dim V, i.e. p(t )v = then =Sp(T )v = Sp(T )S 1 Sv = p(st S 1 )Sv, so Sv null(st S 1 λi) dim V as claimed. As T = S 1 (ST S 1 )S, this also gives {S 1 w : w null(st S 1 λi) dim V } null(t λi) dim V. As S(S 1 w)=w, we deduce that for w null(st S 1 λi) dim V there is in fact v null(t λi) dim V with Sv = w, namely v = S 1 w, so in summary {Sv : v null(t λi) dim V } = null(st S 1 λi) dim V. (iii) Let λ j, j =1,..., k be the distinct eigenvalues of T with multiplicity m 1,..., m k, respectively. That is, dim null(t λ j I) dim V = m j. By what we have shown, the distinct eigenvalues of ST S 1 are also exactly the λ j, the generalized eigenspaces are {Sv : v null(t λ j I) dim V }, and as S null(t λji) dim V : null(t λ ji) dim V null(st S 1 λ j I) dim V is a bijection, hence an isomorphism, we deduce that these generalized eigenspaces also have dimension m j. Thus, k trace T = m j λ j = trace(st S 1 ). Problem 7. (2 points) Suppose that V is a complex inner product space. (i) Suppose T L(V ) and there is a constant C such that Tv C v for all v V. Show that all eigenvalues λ of T satisfy λ C. (ii) Suppose now that T L(V ) in normal, and all eigenvalues satisfy λ C. Show that Tv C v for all v V. (iii) Give an example of an operator T L(C 2 ) for which there is a constant C such that λ C for all eigenvalues λ of T, but there is a vector v C 2 with Tv >C v. Solution 7. (i) Suppose that λ is an eigenvalue of T. Then there is v V, v, such that Tv = λv. Thus, taking norms, C v Tv = λv = λ v. Dividing by v gives the conclusion.
6 MATH 113: PRACTICE FINAL SOLUTIONS 5 (ii) Since T is normal, V has an orthonormal basis (e 1,..., e n ) consisting of eigenvectors of T, with eigenvalue λ 1,..., λ n. Recall that for any v V, v = n v, e j e j, and that for any a j C, n a je j 2 = n a j 2. Now we compute for v V : Tv 2 = T ( v, e j e j ) 2 = v, e j Te j 2 = v, e j λ j e j 2 = v, e j 2 λ j 2 C 2 n v, e j 2 = C 2 v 2. Taking positive square roots gives the conclusion. (iii) Take any non-zero nilpotent operator: the eigenvalues are all, so one can take C =, but the operator is not the zero-operator, so there is some v such that Tv, hence Tv > = v = C v. For example, take T L(C 2 ) given by T (z 1,z 2 ) = (z 2, ), so T 2 =, hence T is nilpotent, but T (, 1) = (1, ). Problem 8. (2 points) (i) Suppose that V, W are inner product spaces. Show that for all T L(V, W ), dim range T = dim range T, dim null T = dim null T + dim W dim V. (ii) Consider the map T : R n R n+1, T (x 1,..., x n ) = (x 1,..., x n, ). Find T explicitly. What is null T? Solution 8. (i) As range T is the orthocomplement of null T in W, dim range T + dim null T = dim W. As range T is the orthocomplement of null T in V, dim range T + dim null T = dim V. By the rank-nullity theorem applied to T, one has dim range T + dim null T = dim V. Subtracting the first of the three displayed equations from the third, we get dim null T dim null T = dim V dim W, which proves the claim regarding nullspaces, while subtracting the second displayed equation from the third gives dim range T dim range T =, which proves the conclusion regarding ranges. (ii) Recall that unless otherwise specified, we have the standard inner product on R p, p = n, n + 1 here. For y =(y 1,..., y n+1 ) R n+1, T y R n is defined by the property that for all x R n n+1 x, T y R n = T x, y R n+1 = (Tx) j y j = Thus, T y =(y 1,...,y n ). Correspondingly x j y j = x, (y 1,..., y n ). null T = {(,...,,y n+1 ): y n+1 R}.
7 6 MATH 113: PRACTICE FINAL SOLUTIONS Problem 9. (Bonus problem: do it only if you have solved all other problems.) If V, W, Z are vector spaces over a field F, and F : V W Z, we say that F is bilinear if F (v 1 + v 2,w)=F (v 1,w)+F (v 2,w), F(cv, w) =cf (v, w) F (v, w 1 + w 2 )=F (v, w 1 )+F (v, w 2 ), F(v, cw) =cf (v, w) for all v, v 1,v 2 V, w, w 1,w 2 W, c F, i.e. if F is linear in both slots. (i) (1 points) Show that the set B(V, W ; Z) of bilinear maps from V W to Z is a vector space over F under the usual addition and multiplication by scalars: (F + G)(v, w) =F (v, w)+g(v, w), (λf )(v, w) =λf (v, w), (v V, w W, c F) make sure to check that F + G and cf are indeed bilinear. (ii) (Bonus problem: do it only if you have solved all other problems.) Recall that V = L(V,F) is the dual of V. For f V, g W, let f g : V W F be the map (f g)(v, w) =f(v)g(w) F. Show that f g B(V, W ; F), and the map j : V W B(V, W ; F), j(f, g) =f g, is bilinear. (iii) (Bonus problem: do it only if you have solved all other problems.) If V, W are finite dimensional with bases (v 1,..., v m ), (w 1,..., w n ), (f 1,...,f m ) is the basis of V dual to (v 1,...,v m ), and (g 1,..., g n ) is the basis of W dual to (w 1,..., w n ), let e ij = f i g j, so e ij (v r,w s ) = if r i or s j, and e ij (v r,w s ) = 1 if r = i and s = j. Show that {e ij : i =1,..., m, j =1,..., n} is a basis for B(V, W ; F). In particular, conclude that dim B(V, W ; F) = (dim V )(dim W ). Remark: One could now define the tensor product of V and W as V W = B(V, W ; F), or the tensor product of V and W, using (V ) = V, as Solution 9. V W = B(V,W, F). (i) First, if F is bilinear and λ F, then (λf )(cv, w) =λf (cv, w) =λ(cf (v, w)) = c(λf (v, w)) = c(λf )(v, w), (λf )(v 1 + v 2,w)=λF (v 1 + v 2,w)=λ(F (v 1,w)+F (v 2,w)) = λf (v 1,w)+λF (v 2,w) = (λf )(v 1,w) + (λf )(v 2,w), so λf is linear in the first slot. Here the first and last equalities in both chains are the definition of λf, the second is that F is linear in the first slot, while the third is using the vector space structure in Z, namely that λ(cz) =c(λz) for all z Z, respectively the distributive law. The calculation that λf is linear in the second slot is identical, with the role of v s and w s interchanged. A different way of phrasing this is the following: for each fixed w W, let Fw (v) =F (v, w), and for each fixed v V let F v (w) =F (v, w). Then the statement that F is bilinear is equivalent to Fw : V Z being linear for each w W and Fv : W Z being linear for each v V. Since (λf ) w (v) = (λf )(v, w) =λf (v, w) =λf w (v), (λf ) w = λf w, so the fact that (λf ) w is linear follows from the fact that λf w is and the latter we have already shown (a scalar multiple of a linear map is linear). A similar calculation for F v shows that λf is bilinear as claimed.
8 MATH 113: PRACTICE FINAL SOLUTIONS 7 Similarly, F + G is bilinear if F and G are because (F + G) w = F w + G w, F w and G w are linear, with analogous formulae for (F + G) v. Now, if U is any set (and Z is a vector space as above), the set M(U; Z) of all maps from U to Z is a vector space with the usual addition and multiplication by scalars: (λf )(u) =λf (u), (F + G)(u) =F (u) +G(u). Now, B(V, W ; Z) is a subset of M(V W ; Z), and we have shown that it is closed under addition and multiplication by scalars. As the -map is bilinear, we deduce that B(V, W ; Z) is a subspace of M(V W ; Z), so in particular is a vector space itself. (ii) (f g) w(v) = (f g)(v, w) =f(v)g(w) =g(w)f(v), so (f g) w = g(w)f. As f is linear, g(w) F, we deduce that (f g) w is linear. The proof that (f g) v is linear is similar, and we deduce that f g is bilinear, so f g B(V, W ; F). Now consider the map j : V W B(V, W ; F), j(f, g) =f g. Then j(λf, g)(v, w) = ((λf) g)(v, w) = (λf)(v)g(w) =λf(v)g(w) = λ(f g)(w) =λj(f, g)(v, w), with a similar calculation for homogeneity in the second slot. Additivity is also similar: j(f 1 + f 2,g)(v, w) = ((f 1 + f 2 ) g)(v, w) = (f 1 + f 2 )(v)g(w) = (f 1 (v)+f 2 (v))g(w) = f 1 (v)g(w)+f 2 (v)g(w) = (f 1 g)(v, w) + (f 2 g)(v, w) =(j(f 1,g)+j(f 2,g))(v, w), with a similar calculation in the second slot. Thus, j is indeed bilinear. (iii) First, i,j a ije ij = means that i,j a ije ij (v, w) = for all v V, w W. Letting v = v r, w = w s we deduce that a rs = for all r, s, so {e ij : i =1,..., m, j = 1,..., n} is linearly independent. To see that it spans, suppose that F is a bilinear map. For v V, w W, write v = i b iv i, w = j c jw j. Then F (v, w) =F ( i b i v i, j c j w j )= i,j b i c j F (v i,w j ) = i,j F (v i,w j )e ij ( r b r v r, s c s w s )= i,j F (v i,w j )e ij (v, w). Thus F = F (v i,w j )e ij, proving that {e ij : i =1,..., m, j =1,..., n} spans. Combined with the already shown linear independence, we deduce that it is a basis, so dim B(V, W ; F) =mn = (dim V )(dim W ), as claimed.
5.) For each of the given sets of vectors, determine whether or not the set spans R 3. Give reasons for your answers.
Linear Algebra - Test File - Spring Test # For problems - consider the following system of equations. x + y - z = x + y + 4z = x + y + 6z =.) Solve the system without using your calculator..) Find the
More informationMATH SOLUTIONS TO PRACTICE MIDTERM LECTURE 1, SUMMER Given vector spaces V and W, V W is the vector space given by
MATH 110 - SOLUTIONS TO PRACTICE MIDTERM LECTURE 1, SUMMER 2009 GSI: SANTIAGO CAÑEZ 1. Given vector spaces V and W, V W is the vector space given by V W = {(v, w) v V and w W }, with addition and scalar
More informationMAT Linear Algebra Collection of sample exams
MAT 342 - Linear Algebra Collection of sample exams A-x. (0 pts Give the precise definition of the row echelon form. 2. ( 0 pts After performing row reductions on the augmented matrix for a certain system
More informationMath 113 Final Exam: Solutions
Math 113 Final Exam: Solutions Thursday, June 11, 2013, 3.30-6.30pm. 1. (25 points total) Let P 2 (R) denote the real vector space of polynomials of degree 2. Consider the following inner product on P
More informationMathematics Department Stanford University Math 61CM/DM Inner products
Mathematics Department Stanford University Math 61CM/DM Inner products Recall the definition of an inner product space; see Appendix A.8 of the textbook. Definition 1 An inner product space V is a vector
More informationLINEAR ALGEBRA REVIEW
LINEAR ALGEBRA REVIEW JC Stuff you should know for the exam. 1. Basics on vector spaces (1) F n is the set of all n-tuples (a 1,... a n ) with a i F. It forms a VS with the operations of + and scalar multiplication
More informationMATH 115A: SAMPLE FINAL SOLUTIONS
MATH A: SAMPLE FINAL SOLUTIONS JOE HUGHES. Let V be the set of all functions f : R R such that f( x) = f(x) for all x R. Show that V is a vector space over R under the usual addition and scalar multiplication
More informationUniversity of Colorado Denver Department of Mathematical and Statistical Sciences Applied Linear Algebra Ph.D. Preliminary Exam May 25th, 2018
University of Colorado Denver Department of Mathematical and Statistical Sciences Applied Linear Algebra Ph.D. Preliminary Exam May 25th, 2018 Name: Exam Rules: This exam lasts 4 hours. There are 8 problems.
More informationMATH 1120 (LINEAR ALGEBRA 1), FINAL EXAM FALL 2011 SOLUTIONS TO PRACTICE VERSION
MATH (LINEAR ALGEBRA ) FINAL EXAM FALL SOLUTIONS TO PRACTICE VERSION Problem (a) For each matrix below (i) find a basis for its column space (ii) find a basis for its row space (iii) determine whether
More informationLINEAR ALGEBRA 1, 2012-I PARTIAL EXAM 3 SOLUTIONS TO PRACTICE PROBLEMS
LINEAR ALGEBRA, -I PARTIAL EXAM SOLUTIONS TO PRACTICE PROBLEMS Problem (a) For each of the two matrices below, (i) determine whether it is diagonalizable, (ii) determine whether it is orthogonally diagonalizable,
More informationMath 308 Practice Test for Final Exam Winter 2015
Math 38 Practice Test for Final Exam Winter 25 No books are allowed during the exam. But you are allowed one sheet ( x 8) of handwritten notes (back and front). You may use a calculator. For TRUE/FALSE
More information1 Invariant subspaces
MATH 2040 Linear Algebra II Lecture Notes by Martin Li Lecture 8 Eigenvalues, eigenvectors and invariant subspaces 1 In previous lectures we have studied linear maps T : V W from a vector space V to another
More informationYORK UNIVERSITY. Faculty of Science Department of Mathematics and Statistics MATH M Test #2 Solutions
YORK UNIVERSITY Faculty of Science Department of Mathematics and Statistics MATH 3. M Test # Solutions. (8 pts) For each statement indicate whether it is always TRUE or sometimes FALSE. Note: For this
More informationMATH 304 Linear Algebra Lecture 34: Review for Test 2.
MATH 304 Linear Algebra Lecture 34: Review for Test 2. Topics for Test 2 Linear transformations (Leon 4.1 4.3) Matrix transformations Matrix of a linear mapping Similar matrices Orthogonality (Leon 5.1
More informationThen x 1,..., x n is a basis as desired. Indeed, it suffices to verify that it spans V, since n = dim(v ). We may write any v V as r
Practice final solutions. I did not include definitions which you can find in Axler or in the course notes. These solutions are on the terse side, but would be acceptable in the final. However, if you
More informationMath 113 Winter 2013 Prof. Church Midterm Solutions
Math 113 Winter 2013 Prof. Church Midterm Solutions Name: Student ID: Signature: Question 1 (20 points). Let V be a finite-dimensional vector space, and let T L(V, W ). Assume that v 1,..., v n is a basis
More informationUniversity of Colorado Denver Department of Mathematical and Statistical Sciences Applied Linear Algebra Ph.D. Preliminary Exam June 8, 2012
University of Colorado Denver Department of Mathematical and Statistical Sciences Applied Linear Algebra Ph.D. Preliminary Exam June 8, 2012 Name: Exam Rules: This is a closed book exam. Once the exam
More information( 9x + 3y. y 3y = (λ 9)x 3x + y = λy 9x + 3y = 3λy 9x + (λ 9)x = λ(λ 9)x. (λ 2 10λ)x = 0
Math 46 (Lesieutre Practice final ( minutes December 9, 8 Problem Consider the matrix M ( 9 a Prove that there is a basis for R consisting of orthonormal eigenvectors for M This is just the spectral theorem:
More informationMATH 235. Final ANSWERS May 5, 2015
MATH 235 Final ANSWERS May 5, 25. ( points) Fix positive integers m, n and consider the vector space V of all m n matrices with entries in the real numbers R. (a) Find the dimension of V and prove your
More information1 Last time: least-squares problems
MATH Linear algebra (Fall 07) Lecture Last time: least-squares problems Definition. If A is an m n matrix and b R m, then a least-squares solution to the linear system Ax = b is a vector x R n such that
More informationMath 224, Fall 2007 Exam 3 Thursday, December 6, 2007
Math 224, Fall 2007 Exam 3 Thursday, December 6, 2007 You have 1 hour and 20 minutes. No notes, books, or other references. You are permitted to use Maple during this exam, but you must start with a blank
More informationMath 4153 Exam 3 Review. The syllabus for Exam 3 is Chapter 6 (pages ), Chapter 7 through page 137, and Chapter 8 through page 182 in Axler.
Math 453 Exam 3 Review The syllabus for Exam 3 is Chapter 6 (pages -2), Chapter 7 through page 37, and Chapter 8 through page 82 in Axler.. You should be sure to know precise definition of the terms we
More informationPRACTICE PROBLEMS FOR THE FINAL
PRACTICE PROBLEMS FOR THE FINAL Here are a slew of practice problems for the final culled from old exams:. Let P be the vector space of polynomials of degree at most. Let B = {, (t ), t + t }. (a) Show
More informationHomework 11 Solutions. Math 110, Fall 2013.
Homework 11 Solutions Math 110, Fall 2013 1 a) Suppose that T were self-adjoint Then, the Spectral Theorem tells us that there would exist an orthonormal basis of P 2 (R), (p 1, p 2, p 3 ), consisting
More informationLinear Algebra Final Exam Solutions, December 13, 2008
Linear Algebra Final Exam Solutions, December 13, 2008 Write clearly, with complete sentences, explaining your work. You will be graded on clarity, style, and brevity. If you add false statements to a
More informationMath 115A: Homework 5
Math 115A: Homework 5 1 Suppose U, V, and W are finite-dimensional vector spaces over a field F, and that are linear a) Prove ker ST ) ker T ) b) Prove nullst ) nullt ) c) Prove imst ) im S T : U V, S
More information235 Final exam review questions
5 Final exam review questions Paul Hacking December 4, 0 () Let A be an n n matrix and T : R n R n, T (x) = Ax the linear transformation with matrix A. What does it mean to say that a vector v R n is an
More informationMATH 20F: LINEAR ALGEBRA LECTURE B00 (T. KEMP)
MATH 20F: LINEAR ALGEBRA LECTURE B00 (T KEMP) Definition 01 If T (x) = Ax is a linear transformation from R n to R m then Nul (T ) = {x R n : T (x) = 0} = Nul (A) Ran (T ) = {Ax R m : x R n } = {b R m
More informationMA 265 FINAL EXAM Fall 2012
MA 265 FINAL EXAM Fall 22 NAME: INSTRUCTOR S NAME:. There are a total of 25 problems. You should show work on the exam sheet, and pencil in the correct answer on the scantron. 2. No books, notes, or calculators
More informationMATH 304 Linear Algebra Lecture 23: Diagonalization. Review for Test 2.
MATH 304 Linear Algebra Lecture 23: Diagonalization. Review for Test 2. Diagonalization Let L be a linear operator on a finite-dimensional vector space V. Then the following conditions are equivalent:
More informationMATH 240 Spring, Chapter 1: Linear Equations and Matrices
MATH 240 Spring, 2006 Chapter Summaries for Kolman / Hill, Elementary Linear Algebra, 8th Ed. Sections 1.1 1.6, 2.1 2.2, 3.2 3.8, 4.3 4.5, 5.1 5.3, 5.5, 6.1 6.5, 7.1 7.2, 7.4 DEFINITIONS Chapter 1: Linear
More informationEigenvalues and Eigenvectors A =
Eigenvalues and Eigenvectors Definition 0 Let A R n n be an n n real matrix A number λ R is a real eigenvalue of A if there exists a nonzero vector v R n such that A v = λ v The vector v is called an eigenvector
More informationMath 350 Fall 2011 Notes about inner product spaces. In this notes we state and prove some important properties of inner product spaces.
Math 350 Fall 2011 Notes about inner product spaces In this notes we state and prove some important properties of inner product spaces. First, recall the dot product on R n : if x, y R n, say x = (x 1,...,
More informationLinear Algebra 2 Spectral Notes
Linear Algebra 2 Spectral Notes In what follows, V is an inner product vector space over F, where F = R or C. We will use results seen so far; in particular that every linear operator T L(V ) has a complex
More informationLecture Notes for Math 414: Linear Algebra II Fall 2015, Michigan State University
Lecture Notes for Fall 2015, Michigan State University Matthew Hirn December 11, 2015 Beginning of Lecture 1 1 Vector Spaces What is this course about? 1. Understanding the structural properties of a wide
More informationMath 113 Solutions: Homework 8. November 28, 2007
Math 113 Solutions: Homework 8 November 28, 27 3) Define c j = ja j, d j = 1 j b j, for j = 1, 2,, n Let c = c 1 c 2 c n and d = vectors in R n Then the Cauchy-Schwarz inequality on Euclidean n-space gives
More informationMATH 31 - ADDITIONAL PRACTICE PROBLEMS FOR FINAL
MATH 3 - ADDITIONAL PRACTICE PROBLEMS FOR FINAL MAIN TOPICS FOR THE FINAL EXAM:. Vectors. Dot product. Cross product. Geometric applications. 2. Row reduction. Null space, column space, row space, left
More informationFinal A. Problem Points Score Total 100. Math115A Nadja Hempel 03/23/2017
Final A Math115A Nadja Hempel 03/23/2017 nadja@math.ucla.edu Name: UID: Problem Points Score 1 10 2 20 3 5 4 5 5 9 6 5 7 7 8 13 9 16 10 10 Total 100 1 2 Exercise 1. (10pt) Let T : V V be a linear transformation.
More information2. Every linear system with the same number of equations as unknowns has a unique solution.
1. For matrices A, B, C, A + B = A + C if and only if A = B. 2. Every linear system with the same number of equations as unknowns has a unique solution. 3. Every linear system with the same number of equations
More informationMATH 315 Linear Algebra Homework #1 Assigned: August 20, 2018
Homework #1 Assigned: August 20, 2018 Review the following subjects involving systems of equations and matrices from Calculus II. Linear systems of equations Converting systems to matrix form Pivot entry
More information(v, w) = arccos( < v, w >
MA322 Sathaye Notes on Inner Products Notes on Chapter 6 Inner product. Given a real vector space V, an inner product is defined to be a bilinear map F : V V R such that the following holds: For all v
More informationLinear Algebra- Final Exam Review
Linear Algebra- Final Exam Review. Let A be invertible. Show that, if v, v, v 3 are linearly independent vectors, so are Av, Av, Av 3. NOTE: It should be clear from your answer that you know the definition.
More information2.2. Show that U 0 is a vector space. For each α 0 in F, show by example that U α does not satisfy closure.
Hints for Exercises 1.3. This diagram says that f α = β g. I will prove f injective g injective. You should show g injective f injective. Assume f is injective. Now suppose g(x) = g(y) for some x, y A.
More informationLinear Algebra Lecture Notes-II
Linear Algebra Lecture Notes-II Vikas Bist Department of Mathematics Panjab University, Chandigarh-64 email: bistvikas@gmail.com Last revised on March 5, 8 This text is based on the lectures delivered
More informationElementary Linear Algebra Review for Exam 2 Exam is Monday, November 16th.
Elementary Linear Algebra Review for Exam Exam is Monday, November 6th. The exam will cover sections:.4,..4, 5. 5., 7., the class notes on Markov Models. You must be able to do each of the following. Section.4
More informationGQE ALGEBRA PROBLEMS
GQE ALGEBRA PROBLEMS JAKOB STREIPEL Contents. Eigenthings 2. Norms, Inner Products, Orthogonality, and Such 6 3. Determinants, Inverses, and Linear (In)dependence 4. (Invariant) Subspaces 3 Throughout
More informationMATH 423 Linear Algebra II Lecture 12: Review for Test 1.
MATH 423 Linear Algebra II Lecture 12: Review for Test 1. Topics for Test 1 Vector spaces (F/I/S 1.1 1.7, 2.2, 2.4) Vector spaces: axioms and basic properties. Basic examples of vector spaces (coordinate
More informationMath 4153 Exam 1 Review
The syllabus for Exam 1 is Chapters 1 3 in Axler. 1. You should be sure to know precise definition of the terms we have used, and you should know precise statements (including all relevant hypotheses)
More informationMath 520 Exam 2 Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008
Math 520 Exam 2 Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008 Exam 2 will be held on Tuesday, April 8, 7-8pm in 117 MacMillan What will be covered The exam will cover material from the lectures
More informationhomogeneous 71 hyperplane 10 hyperplane 34 hyperplane 69 identity map 171 identity map 186 identity map 206 identity matrix 110 identity matrix 45
address 12 adjoint matrix 118 alternating 112 alternating 203 angle 159 angle 33 angle 60 area 120 associative 180 augmented matrix 11 axes 5 Axiom of Choice 153 basis 178 basis 210 basis 74 basis test
More informationIMPORTANT DEFINITIONS AND THEOREMS REFERENCE SHEET
IMPORTANT DEFINITIONS AND THEOREMS REFERENCE SHEET This is a (not quite comprehensive) list of definitions and theorems given in Math 1553. Pay particular attention to the ones in red. Study Tip For each
More informationIMPORTANT DEFINITIONS AND THEOREMS REFERENCE SHEET
IMPORTANT DEFINITIONS AND THEOREMS REFERENCE SHEET This is a (not quite comprehensive) list of definitions and theorems given in Math 1553. Pay particular attention to the ones in red. Study Tip For each
More informationContents. Preface for the Instructor. Preface for the Student. xvii. Acknowledgments. 1 Vector Spaces 1 1.A R n and C n 2
Contents Preface for the Instructor xi Preface for the Student xv Acknowledgments xvii 1 Vector Spaces 1 1.A R n and C n 2 Complex Numbers 2 Lists 5 F n 6 Digression on Fields 10 Exercises 1.A 11 1.B Definition
More informationGlossary of Linear Algebra Terms. Prepared by Vince Zaccone For Campus Learning Assistance Services at UCSB
Glossary of Linear Algebra Terms Basis (for a subspace) A linearly independent set of vectors that spans the space Basic Variable A variable in a linear system that corresponds to a pivot column in the
More information1. What is the determinant of the following matrix? a 1 a 2 4a 3 2a 2 b 1 b 2 4b 3 2b c 1. = 4, then det
What is the determinant of the following matrix? 3 4 3 4 3 4 4 3 A 0 B 8 C 55 D 0 E 60 If det a a a 3 b b b 3 c c c 3 = 4, then det a a 4a 3 a b b 4b 3 b c c c 3 c = A 8 B 6 C 4 D E 3 Let A be an n n matrix
More informationDEPARTMENT OF MATHEMATICS
Name(Last/First): GUID: DEPARTMENT OF MATHEMATICS Ma322005(Sathaye) - Final Exam Spring 2017 May 3, 2017 DO NOT TURN THIS PAGE UNTIL YOU ARE INSTRUCTED TO DO SO. Be sure to show all work and justify your
More informationThroughout these notes we assume V, W are finite dimensional inner product spaces over C.
Math 342 - Linear Algebra II Notes Throughout these notes we assume V, W are finite dimensional inner product spaces over C 1 Upper Triangular Representation Proposition: Let T L(V ) There exists an orthonormal
More informationReview of linear algebra
Review of linear algebra 1 Vectors and matrices We will just touch very briefly on certain aspects of linear algebra, most of which should be familiar. Recall that we deal with vectors, i.e. elements of
More information80 min. 65 points in total. The raw score will be normalized according to the course policy to count into the final score.
This is a closed book, closed notes exam You need to justify every one of your answers unless you are asked not to do so Completely correct answers given without justification will receive little credit
More informationDimension. Eigenvalue and eigenvector
Dimension. Eigenvalue and eigenvector Math 112, week 9 Goals: Bases, dimension, rank-nullity theorem. Eigenvalue and eigenvector. Suggested Textbook Readings: Sections 4.5, 4.6, 5.1, 5.2 Week 9: Dimension,
More informationLINEAR ALGEBRA MICHAEL PENKAVA
LINEAR ALGEBRA MICHAEL PENKAVA 1. Linear Maps Definition 1.1. If V and W are vector spaces over the same field K, then a map λ : V W is called a linear map if it satisfies the two conditions below: (1)
More informationA PRIMER ON SESQUILINEAR FORMS
A PRIMER ON SESQUILINEAR FORMS BRIAN OSSERMAN This is an alternative presentation of most of the material from 8., 8.2, 8.3, 8.4, 8.5 and 8.8 of Artin s book. Any terminology (such as sesquilinear form
More informationUniversity of Colorado Denver Department of Mathematical and Statistical Sciences Applied Linear Algebra Ph.D. Preliminary Exam January 22, 2016
University of Colorado Denver Department of Mathematical and Statistical Sciences Applied Linear Algebra PhD Preliminary Exam January 22, 216 Name: Exam Rules: This exam lasts 4 hours There are 8 problems
More informationLast name: First name: Signature: Student number:
MAT 2141 The final exam Instructor: K. Zaynullin Last name: First name: Signature: Student number: Do not detach the pages of this examination. You may use the back of the pages as scrap paper for calculations,
More informationLinear Algebra. Min Yan
Linear Algebra Min Yan January 2, 2018 2 Contents 1 Vector Space 7 1.1 Definition................................. 7 1.1.1 Axioms of Vector Space..................... 7 1.1.2 Consequence of Axiom......................
More informationMath 110, Spring 2015: Midterm Solutions
Math 11, Spring 215: Midterm Solutions These are not intended as model answers ; in many cases far more explanation is provided than would be necessary to receive full credit. The goal here is to make
More informationMath 4A Notes. Written by Victoria Kala Last updated June 11, 2017
Math 4A Notes Written by Victoria Kala vtkala@math.ucsb.edu Last updated June 11, 2017 Systems of Linear Equations A linear equation is an equation that can be written in the form a 1 x 1 + a 2 x 2 +...
More informationNo books, no notes, no calculators. You must show work, unless the question is a true/false, yes/no, or fill-in-the-blank question.
Math 304 Final Exam (May 8) Spring 206 No books, no notes, no calculators. You must show work, unless the question is a true/false, yes/no, or fill-in-the-blank question. Name: Section: Question Points
More informationStudy Guide for Linear Algebra Exam 2
Study Guide for Linear Algebra Exam 2 Term Vector Space Definition A Vector Space is a nonempty set V of objects, on which are defined two operations, called addition and multiplication by scalars (real
More informationMath 113 Homework 5. Bowei Liu, Chao Li. Fall 2013
Math 113 Homework 5 Bowei Liu, Chao Li Fall 2013 This homework is due Thursday November 7th at the start of class. Remember to write clearly, and justify your solutions. Please make sure to put your name
More information08a. Operators on Hilbert spaces. 1. Boundedness, continuity, operator norms
(February 24, 2017) 08a. Operators on Hilbert spaces Paul Garrett garrett@math.umn.edu http://www.math.umn.edu/ garrett/ [This document is http://www.math.umn.edu/ garrett/m/real/notes 2016-17/08a-ops
More informationLINEAR ALGEBRA REVIEW
LINEAR ALGEBRA REVIEW When we define a term, we put it in boldface. This is a very compressed review; please read it very carefully and be sure to ask questions on parts you aren t sure of. x 1 WedenotethesetofrealnumbersbyR.
More informationFINAL EXAM Ma (Eakin) Fall 2015 December 16, 2015
FINAL EXAM Ma-00 Eakin Fall 05 December 6, 05 Please make sure that your name and GUID are on every page. This exam is designed to be done with pencil-and-paper calculations. You may use your calculator
More informationThe value of a problem is not so much coming up with the answer as in the ideas and attempted ideas it forces on the would be solver I.N.
Math 410 Homework Problems In the following pages you will find all of the homework problems for the semester. Homework should be written out neatly and stapled and turned in at the beginning of class
More informationSolutions to Final Exam
Solutions to Final Exam. Let A be a 3 5 matrix. Let b be a nonzero 5-vector. Assume that the nullity of A is. (a) What is the rank of A? 3 (b) Are the rows of A linearly independent? (c) Are the columns
More informationQuizzes for Math 304
Quizzes for Math 304 QUIZ. A system of linear equations has augmented matrix 2 4 4 A = 2 0 2 4 3 5 2 a) Write down this system of equations; b) Find the reduced row-echelon form of A; c) What are the pivot
More informationDEPARTMENT OF MATHEMATICS
Name(Last/First): GUID: DEPARTMENT OF MATHEMATICS Ma322005(Sathaye) - Final Exam Spring 2017 May 3, 2017 DO NOT TURN THIS PAGE UNTIL YOU ARE INSTRUCTED TO DO SO. Be sure to show all work and justify your
More informationDEPARTMENT OF MATHEMATICS
Name(Last/First): GUID: DEPARTMENT OF MATHEMATICS Ma322005(Sathaye) - Final Exam Spring 2017 May 3, 2017 DO NOT TURN THIS PAGE UNTIL YOU ARE INSTRUCTED TO DO SO. Be sure to show all work and justify your
More informationSolving a system by back-substitution, checking consistency of a system (no rows of the form
MATH 520 LEARNING OBJECTIVES SPRING 2017 BROWN UNIVERSITY SAMUEL S. WATSON Week 1 (23 Jan through 27 Jan) Definition of a system of linear equations, definition of a solution of a linear system, elementary
More information6 Inner Product Spaces
Lectures 16,17,18 6 Inner Product Spaces 6.1 Basic Definition Parallelogram law, the ability to measure angle between two vectors and in particular, the concept of perpendicularity make the euclidean space
More informationMath 113 Midterm Exam Solutions
Math 113 Midterm Exam Solutions Held Thursday, May 7, 2013, 7-9 pm. 1. (10 points) Let V be a vector space over F and T : V V be a linear operator. Suppose that there is a non-zero vector v V such that
More informationMath Exam 2, October 14, 2008
Math 96 - Exam 2, October 4, 28 Name: Problem (5 points Find all solutions to the following system of linear equations, check your work: x + x 2 x 3 2x 2 2x 3 2 x x 2 + x 3 2 Solution Let s perform Gaussian
More informationLecture 19: Polar and singular value decompositions; generalized eigenspaces; the decomposition theorem (1)
Lecture 19: Polar and singular value decompositions; generalized eigenspaces; the decomposition theorem (1) Travis Schedler Thurs, Nov 17, 2011 (version: Thurs, Nov 17, 1:00 PM) Goals (2) Polar decomposition
More informationMath 312 Final Exam Jerry L. Kazdan May 5, :00 2:00
Math 32 Final Exam Jerry L. Kazdan May, 204 2:00 2:00 Directions This exam has three parts. Part A has shorter questions, (6 points each), Part B has 6 True/False questions ( points each), and Part C has
More informationMATH 260 LINEAR ALGEBRA EXAM III Fall 2014
MAH 60 LINEAR ALGEBRA EXAM III Fall 0 Instructions: the use of built-in functions of your calculator such as det( ) or RREF is permitted ) Consider the table and the vectors and matrices given below Fill
More informationA linear algebra proof of the fundamental theorem of algebra
A linear algebra proof of the fundamental theorem of algebra Andrés E. Caicedo May 18, 2010 Abstract We present a recent proof due to Harm Derksen, that any linear operator in a complex finite dimensional
More informationDM554 Linear and Integer Programming. Lecture 9. Diagonalization. Marco Chiarandini
DM554 Linear and Integer Programming Lecture 9 Marco Chiarandini Department of Mathematics & Computer Science University of Southern Denmark Outline 1. More on 2. 3. 2 Resume Linear transformations and
More informationReview problems for MA 54, Fall 2004.
Review problems for MA 54, Fall 2004. Below are the review problems for the final. They are mostly homework problems, or very similar. If you are comfortable doing these problems, you should be fine on
More informationMath 414: Linear Algebra II, Fall 2015 Final Exam
Math 414: Linear Algebra II, Fall 2015 Final Exam December 17, 2015 Name: This is a closed book single author exam. Use of books, notes, or other aids is not permissible, nor is collaboration with any
More informationA linear algebra proof of the fundamental theorem of algebra
A linear algebra proof of the fundamental theorem of algebra Andrés E. Caicedo May 18, 2010 Abstract We present a recent proof due to Harm Derksen, that any linear operator in a complex finite dimensional
More informationMATH PRACTICE EXAM 1 SOLUTIONS
MATH 2359 PRACTICE EXAM SOLUTIONS SPRING 205 Throughout this exam, V and W will denote vector spaces over R Part I: True/False () For each of the following statements, determine whether the statement is
More informationALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA
ALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA Kent State University Department of Mathematical Sciences Compiled and Maintained by Donald L. White Version: August 29, 2017 CONTENTS LINEAR ALGEBRA AND
More informationGeneralized eigenspaces
Generalized eigenspaces November 30, 2012 Contents 1 Introduction 1 2 Polynomials 2 3 Calculating the characteristic polynomial 5 4 Projections 7 5 Generalized eigenvalues 10 6 Eigenpolynomials 15 1 Introduction
More informationx 3y 2z = 6 1.2) 2x 4y 3z = 8 3x + 6y + 8z = 5 x + 3y 2z + 5t = 4 1.5) 2x + 8y z + 9t = 9 3x + 5y 12z + 17t = 7
Linear Algebra and its Applications-Lab 1 1) Use Gaussian elimination to solve the following systems x 1 + x 2 2x 3 + 4x 4 = 5 1.1) 2x 1 + 2x 2 3x 3 + x 4 = 3 3x 1 + 3x 2 4x 3 2x 4 = 1 x + y + 2z = 4 1.4)
More informationLinear Algebra Practice Problems
Linear Algebra Practice Problems Page of 7 Linear Algebra Practice Problems These problems cover Chapters 4, 5, 6, and 7 of Elementary Linear Algebra, 6th ed, by Ron Larson and David Falvo (ISBN-3 = 978--68-78376-2,
More informationNATIONAL UNIVERSITY OF SINGAPORE MA1101R
Student Number: NATIONAL UNIVERSITY OF SINGAPORE - Linear Algebra I (Semester 2 : AY25/26) Time allowed : 2 hours INSTRUCTIONS TO CANDIDATES. Write down your matriculation/student number clearly in the
More informationPractice Midterm Solutions, MATH 110, Linear Algebra, Fall 2013
Student ID: Circle your GSI and section: If none of the above, please explain: Scerbo 8am 200 Wheeler Scerbo 9am 3109 Etcheverry McIvor 12pm 3107 Etcheverry McIvor 11am 3102 Etcheverry Mannisto 12pm 3
More informationMath 369 Exam #2 Practice Problem Solutions
Math 369 Exam #2 Practice Problem Solutions 2 5. Is { 2, 3, 8 } a basis for R 3? Answer: No, it is not. To show that it is not a basis, it suffices to show that this is not a linearly independent set.
More informationNumerical Linear Algebra
University of Alabama at Birmingham Department of Mathematics Numerical Linear Algebra Lecture Notes for MA 660 (1997 2014) Dr Nikolai Chernov April 2014 Chapter 0 Review of Linear Algebra 0.1 Matrices
More informationLinear algebra 2. Yoav Zemel. March 1, 2012
Linear algebra 2 Yoav Zemel March 1, 2012 These notes were written by Yoav Zemel. The lecturer, Shmuel Berger, should not be held responsible for any mistake. Any comments are welcome at zamsh7@gmail.com.
More information