#### Exercise 550|| Solution

Given a square matrix A and a vector V:

Give the Python code numpy which calculates the product AV (the matrix product and not the term to term product).

#### Exercise 551 || Solution

Given two matrixes A and B:

Give the numpy python code which calculates t(B)A (the inner product or dot product)

#### Exercise 552 || Solution

For Given Matrix A:

- Give the python numpy code that compute the reverse B of matrix A
- To ensure the result, calculate B.dot (A)

#### Exercise 553 || Solution

Give the Python code matplotlib which draws the line D (A, B) passing through the two points A (1,2) and B (2,3).

#### Exercise 554 || Solution

Give the python code numpy which generates the matrix:

A = np.array ([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) via the linespace() method.

#### Exercise 555 || Solution

Give the python code numpy which transforms the matrix:

A = np.array ([1, 2, 3, 4, 5, 6, 7, 8, 9]) into matrix :

B = np.array([[1, 2, 3], [4, 5, 6],[7, 8, 9]])

#### Exercise 556 || Solution

By using the arange() method, giv a numpy python code to get the matrix: A = np.array([ 1 3 5 7 9 11 13]).

#### Exercise 557 Solution

Consider the following matrix:

A = np.array ([[3, -1], [-1, 4], [7,11]])

Give the Python code numpy which displays the rows of the matrix as follows:

*line number 1 is: [3 -1] *

*line number 2 is: [-1 4] *

*line number 3 is: [7 11]*

#### Exercise 558 || Solution

give the python code numpy which changes the value 11 to 22 in the matrix:

A = = np.array ([[5, 2],

[3, 11],

[8.4]])