Linear Algebra: Eigenvalues, Eigenvectors, Orthogonal Complements, and Differential Equati, Exams of Linear Algebra

A collection of problems from chapters 5 and 6 of a linear algebra course, covering topics such as inner product spaces, eigenvalues and eigenvectors, orthogonal complements, and least squares problems. Students are asked to find eigenvalues and eigenspaces, decide diagonalizability, compute matrix powers and exponentials, and solve systems of linear differential equations.

Typology: Exams

Pre 2010

Uploaded on 07/29/2009

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Math310: Sample Problems from Chapters 5 and 6
The following topics from Chapters 5 and 6 will be covered on the final exam
Inner product spaces: finding angles, scalar and vector projections; deciding
whether a set of vectors is an orthonormal/orthogonal set; Gram-Schmidt or-
thogonalization process; projecting a vector onto a subspace.
Rnwith the standard scalar product: deciding whether two subspaces are orthog-
onal; finding orthogonal complement; least squares problems (projecting a vector
onto a subspace).
Eigenvalues and eigenvectors: finding eigenvalues and eigenspaces of a matrix; de-
ciding whether a matrix is diagonalizable; finding a representation A=XDX 1
when possible; computing rational powers of a matrix and matrix exponential eA;
solving systems of linear differential equations.
Problem 1. Find the eigenvalues and the corresponding eigenspaces for each of the
following matrices. Decide whether each of the following matrices is diagonalizable or
not. If possible find a representation A=XDX1, where Dis a diagonal matrix.
1 1 1
1 1 1
1 1 1
,
1 2 1
0 3 1
0 5 1
,31
1 1 ,1 1
2 3
Problem 2. For each diagonalizable matrix from Problem 1 compute A6, find Bsuch
that B3=A, evaluate eAt.
Problem 3. Use the definition of the matrix exponential to compute eAt for each of the
following matrices:
1 1
0 1 ,1 1
11
Problem 4. Find the general solution to Y0=AY . Solve the initial value problem.
a) A=
1 1 1
1 1 1
1 1 1
, Y (0) =
1
0
2
b) A=1 1
2 3 , Y (0) = 1
2
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Math310: Sample Problems from Chapters 5 and 6

The following topics from Chapters 5 and 6 will be covered on the final exam

  • Inner product spaces: finding angles, scalar and vector projections; deciding whether a set of vectors is an orthonormal/orthogonal set; Gram-Schmidt or- thogonalization process; projecting a vector onto a subspace.
  • Rn^ with the standard scalar product: deciding whether two subspaces are orthog- onal; finding orthogonal complement; least squares problems (projecting a vector onto a subspace).
  • Eigenvalues and eigenvectors: finding eigenvalues and eigenspaces of a matrix; de- ciding whether a matrix is diagonalizable; finding a representation A = XDX−^1 when possible; computing rational powers of a matrix and matrix exponential eA; solving systems of linear differential equations.

Problem 1. Find the eigenvalues and the corresponding eigenspaces for each of the following matrices. Decide whether each of the following matrices is diagonalizable or not. If possible find a representation A = XDX−^1 , where D is a diagonal matrix.

 

Problem 2. For each diagonalizable matrix from Problem 1 compute A^6 , find B such that B^3 = A, evaluate eAt.

Problem 3. Use the definition of the matrix exponential to compute eAt^ for each of the following matrices:

( 1 1 0 1

Problem 4. Find the general solution to Y ′^ = AY. Solve the initial value problem.

a) A =

 , Y (0) =

b) A =

, Y (0) =

c) A =

, Y (0) =

d) A =

, Y (0) =

Problem 5. Translate the second order system

y′′ 1 = 2y 1 − y 2 + y 2 ′ y′′ 2 = y 1 − 2 y 2 + y 1 ′

into a first order system of linear differential equations.

Problem 6. For the given vectors u and v in the given inner product space compute the angle between u and v and find the scalar and vector projections of u onto v.

a) R^4 with the weighted inner product, w = (1, 2 , 1 , 2)T^ and u = (1, − 1 , 0 , 1)T^ , v = (− 1 , 0 , 1 , 1)T^.

b) C[0, 1] with the inner product defined by < f, g >=

0 f^ (x)g(x)dx^ and^ u^ =^ x^ + 1, v = x − 2.

Problem 7. For the given inner product space use the Gram-Schmidt orthogonalization process to find an orthonormal basis of S. Check your answer: show that the set you found is orthonormal. Find the projection p of b onto S. Then the distance from b to S is the norm of b − p. Determine the distance from b to S.

a) R^4 with the standard scalar product, S = Span

and

b =

b) C[− 1 , 1] with the inner product defined by < f, g >= (^12)

− 1 f^ (x)g(x)dx, S = Span{ 1 , x, x^2 } and b = x^3.