4.2 Final exam
4.2.1 Problems listing
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4.2.2 Problem 1
Solve the following system of equations and write the solution as a parametric vector form
Solution
In matrix form , the above system is
The augmented matrix is Hence original system (1) becomes
The above shows that is a free variable and are basic variables. Second row gives or or . First
row gives or or . Hence the solution is
The above is the solution in parametric vector form. For any value of the parameter , a solution
exist.
4.2.3 Problem 2
Compute the determinant using a cofactor expansion solution
Expanding along the last row since it has most number of zeros gives (only the element )
4.2.4 Problem 3
Let
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a
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is in NullSpace of ? Justify your answer.
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b
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Is in columnspace of ? Justify your answer.
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c
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Determine the rank and the Nullity of . Show your work
solution
4.2.4.1 Part a
For an matrix, the solution set corresponding to is called the NullSpace. Therefore we need to
first find this solution set by solving
The augmented matrix is Hence (1) becomes
The above shows that is free variable and are basic variables. Second row gives or . First row
gives or or . Hence the solution is
Now we are ready to answer the question if is in the NullSpace. In other words, does there exist
which make . It is clear there is no such . To show this, looking at the second row, it says or .
But third row says . Therefore there is no which makes in NullSpace. Hence is not in
NullSpace
4.2.4.2 Part b
The columnspace of is the set of all linear combinations of the columns of . The basis for the
columnspace are columns of that correspond to the pivot columns are doing the above REF.
From part we found that column are the pivot columns. Hence the basis of columnspace of are
Hence the columnspace of is two dimensional subspace of . To find if is in columnspace
of , we need to find if there exists a linear combination of these basis which gives .
Therefore we need to solve For to see if a solution exist. In matrix form the above
becomes
The augmented matrix is Hence (1) becomes Therefore the solution is Therefore a solution
exists. This means This means is in the columnspace of .
4.2.4.3 Part c
The rank of is the dimension of the columnspace of . Which is the same as the number of pivot
columns found. In this case, it is as found in part b above. Hence .
Nullity of is the dimension of the nullspace of . From part (a) we found that the nullspace of is
given by the one parameter vector . Hence the dimension is . It is the number of the free
variables. Therefore Nullity of . To verify this, we can use the rank-nullity theorem, which says
for a matrix , Since and since then .
4.2.5 Problem 4
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a
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Using the definition, verify that the given transformation is linear transformation
defined by
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b
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Find the kernel of
solution
4.2.5.1 Part a
The transformation is linear if
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for all in
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for all scalars and in
To show property 1: By linearity of second derivatives (and since are in ) the above becomes
But and , Hence the above becomes To show property 2: But since is a scalar, we can move it
outside the derivative and the above becomes
But . Hence the above becomes Both properties are satisfied. Hence is linear transformation.
4.2.5.2 Part b
The kernel of is . In this case and . Hence we need to find all , such that . Which is the same as
saying all which satisfies . Hence is the solution of this ode.
This is linear constant coefficient ode. The characteristic equation is . The solutions are . Hence
the basis functions are (assuming the independent variable is ), or using Euler relation .
Therefore the solution is linear combination of these basis given by where are arbitrary
constants. Hence
4.2.6 Problem 5
Solve
solution
Writing the ODE as Where . Checking if the ODE is exact
Since , then it is exact in some region . Let there exists constant function which satisfies
For all in . Integrating (1) w.r.t. gives
Taking derivative of the above w.r.t. gives
Comparing (4) and (2) gives
Hence a constant. Substituting this in (3) gives But . Combining the constants into one
constant, say , the above becomes Solving for gives For
4.2.7 Problem 6
Using the method of undermined coefficients, find the general solution of the given differential
equation
solution
The solution is Where is the solution to the homogenous ode and is a particular solution to
the ode. We start by finding . Since this is linear second order with constant coefficient,
then the characteristic equation method is used. The characteristic equation for is
Hence the roots are . Therefore the basis set of solutions for is the set
And is linear combination of these basis. Therefore
Looking at RHS of (1) shows it is linear combination of basis . For each basis in this list, we
generate all possible derivatives. Which gives (ignoring sign changes and any leading constants as
they will be parts of the unknowns to be found later on). This results in the following
list
Now we compare each basis in (2) with each basis in (4) to see if there is any duplication. We see
that is in (4) as well in (2). We now multiply in (4) by an extra and obtain new
list
We repeat this process again, checking if (2) still has any duplication in (4A). There are no
duplication now. Hence the trial solution is linear combination of the basis in (4A). Which
gives
To determine , we substitute back in the ODE (1) and solve for these unknowns by comparing
terms.
Substituting (5,6,7) into the ODE (1) gives
Which simplifies to
Comparing terms on each side gives 3 equations to solve for
First equation gives . Multiplying second equation by and adding the result to third equation
gives
Adding gives
From we now find , or . Hence the particular solution (5) becomes
Substituting (8) and (3) in gives the final solution as
4.2.8 Problem 7
Use the Laplace transform to solve the given initial-value problems. You can use the table of
transformation
solution
Taking the Laplace transform of both sides of gives (using linearity)
Assuming , and using the property that And from table 10.2.1 , then the ode becomes
Using partial fractions on the first term in the RHS above gives
Therefore Comparing terms gives
In matrix form the above is
The augmented matrix is gives Hence (2) becomes
Therefore, since now in RREF form, the solution is Hence
Substituting (4) back in (1A) gives
Now we will use tables to do the inverse Laplace transform. From table 10.2.1
Using this result in (5) and since then (5) becomes
4.2.9 Problem 8
Find a series solution in powers of of the differential equation
solution
Let the solution be
Then
And
Substituting (1,2,3) into the given ODE gives
Now we make all powers of the same by rewriting and . The way the above is done is by using
the rule: When adding a value to the summation index inside the sum, then we must at same
time subtract the same value from the starting index .
Hence (3A) now becomes To be able to compare coefficients of , we expand up to the sums in
order to make all sums start from . This gives
Now we are to compare coefficients on each power of . The above gives the three equations
First equation above gives Second equation in (4) gives And the third equation in (4) gives the
recursive equation which allows us to find all after these Or
Therefore, for the above gives But , therefore the above becomes
And for (5) gives But , the above becomes
And for (5) gives But and . The above becomes And so on. Therefore, from (1)
Therefore
The series solution above contains two unknowns . There are the same as the constant of
integrations. Since this is a second order ODE, then there will be two unknowns in the general
solutions. These can be found from initial conditions. For example, assuming . Then from (8) at ,
it gives . Taking one derivative of (8) gives
At the above becomes . Therefore (8) can be written as
4.2.10 Problem 9
a) Determine all the equilibrium points of the given system. b) Select two equilibrium points and
classify them as saddle, node, spiral or center and whether they are stable or unstable.
solution
4.2.10.1 Part a
equilibrium points are the solutions in of
Which can be written as
From (1), we see that
is a solution and or
Is another solution. For each value in (3,4), now we solve for from (2). When then (2) becomes
Which has solutions . Therefore and are two solutions found so far. And when then (2)
becomes
Which has solutions and . When then gives . Therefore is a solution, and when
then gives . Hence is another solution. Putting all these together gives the solutions
as
4.2.10.2 Part b
The given system is matrix form is
The Jacobian matrix for the system is given by the gradient of
Selecting points and for analysis.
At Point the linearized system matrix is the Jacobian matrix evaluated at this equilibrium
point. Hence
The eigenvalues are found by solving or
Hence and . Since both eigenvalues are positive, then this is unstable critical point. It is a
negative attractor also called a node.
At Point the linearized system matrix is the Jacobian matrix evaluated at this equilibrium
point. Hence
The eigenvalues are found by solving or
Hence and . Since one eigenvalue is positive, and one eigenvalue is negative, then this is unstable
critical point. It is a saddle point.