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Here, we will aim to provide you with a number of exercises and solutions with a varying degree of difficulty. There will also occur exercises that requires you to use functions that has not yet been described in Matrices and linear algebra.

If you have a suggestion on a topic you would like to see some more exercises (or tutorials) on, please send us a mail or leave a post on our Forum. We are more than happy to recieve feedback.

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Determinant

Exercise 1: Simple matrix

Compute the determinant

Solution: Simple matrix
import numpy as np 

# Defining the matrix
A = np.array([[1, 3, 2],
            [4, 1, 3],
            [2, 5, 2]])

det = np.linalg.det(A)
print(det)


# Output:
16.999999999999993   # e.i. det(A) = 17

Exercise 2: Complex numbers

This example is taken from an example exam in TMA4110 - Calculus 3, check out the exam and the solution for hand calculations (only in norwegain). This is task 4.

Comput the determinant

Solution: Complex Numbers
import numpy as np 

# Defining the matrix
# Notice how we can define complex numbers in python by adding the letter "j"
# "j" is used to denote an imaginary unit instead of "i", because Python follows engineering,
# where "i" denotes the electric current as a function of time (especially electrial engineering)
A = np.array([[3, 1-1j, 1j, 4],
            [3, 1, 1-2j, 4+7j],
            [6j, 2+2j, -2, 3j],
            [-3, -1+1j, 1, 3-4j]], dtype=complex)

det = np.linalg.det(A)
print(det)


# Output:
(-15-15j)   # e.i. det(A) = -15-15i

Inverse

Exercise 1

Find the inverse of the matrix


Solution
import numpy as np 

A = np.array([[2, 2, 0],
            [0, 0, 1],
            [4, 2, 0]])
inv_A = np.linalg.inv(A)
print(inv_A)


# Output:
[[-0.5  0.   0.5]
 [ 1.   0.  -0.5]
 [ 0.   1.   0. ]]

Least squares solution to a linear system

Exercise 1

This example is taken from an example exam in TMA4110 - Calculus 3, check out the exam and the solution for hand calculations (only in norwegain). This is task 5.

Use the least squares method to find an approximation to the linear system

First, lets write the equations as matrices on the format , where

To solve the problem in NumPy, we will use the function numpy.linalg.lstsq (or with SciPys scipy.linalg.lstsq) which is currently not described in Matrices and linear algebra. We recommend you to read the function documentation before proceeding.

Solution
import numpy as np 

# Defining the matrices
A = np.array([[1, 2],
            [3, 4],
            [5, 6]])
b = np.array([[1],[2],[3]])

x, residuals, rank, s = np.linalg.lstsq(A, b, rcond=None)   
# "rcond=None" sets new default for rcond, see function documentation

print(x)    # "x" is the solution


# Output:
[[-5.97106181e-17]
 [ 5.00000000e-01]]

We can interpret this such as the least squares solution is

Solving a linear matrix equation

Exercise 1

Solve the following system of linear equations:

Solution


Exercise 2
Solve the following system of linear equations:
Solution




























                
        
    
        


                        
    





    

	

		
    
    
            
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