**Problem 1.** Write a module `symtoep`

for creating various symmetric Toeplitz matrices with the following functions:

`general(L)`

that returns a symmetric Toeplitz matrix with the first row equal to the elements of the list`L`

.`tridiagonal(n, d, sd)`

that returns a tridiagonal symmetric Toeplitz matrix of order`n`

, with the float number`d`

on the diagonal and the float number`sd`

right below and above it (the rest of the elements are zero).`filled(n, d, nd)`

that returns a symmetric Toeplitz matrix of order`n`

, with the float number`d`

on the diagonal and all the non-diagonal elements equal to`nd`

.`menu()`

that displays a choice`Load a symmetric Toeplitz matrix: 1. General symmetric Toeplitz matrix 2. Tridiagonal symmetric Toeplitz matrix 3. Filled symmetric Toeplitz matrix Your choice (1-3):`

and asks the user to choose. Then,

- if the user chooses
`"1"`

, the function asks for a list`L`

of numbers (preferably as a string of comma-separated floats, but you can use some other method as well), and then returns`general(L)`

, - if the user chooses
`"2"`

, the function asks for an integer`n`

and floats`d`

and`sd`

, and returns`tridiagonal(n, d, sd)`

. - if the user chooses
`"3"`

, the function asks for an integer`n`

and floats`d`

and`nd`

, and returns`filled(n, d, sd)`

. - if the user chooses anything else, the choice is redisplayed and user is asked to choose again.

- if the user chooses

When run like a program, this module should do nothing.

Toeplitz matrices are those that have constant elements on all diagonals.

- An example of a symmetric Toeplitz matrix of order $5$: $$\begin{bmatrix} 1 & 2 & 3 & 4 & 5 \\ 2 & 1 & 2 & 3 & 4 \\ 3 & 2 & 1 & 2 & 3 \\ 4 & 3 & 2 & 1 & 2 \\ 5 & 4 & 3 & 2 & 1 \end{bmatrix}$$
- An example of a tridiagonal (all elements other than those on the main and its neighbour diagonals are zero) symmetric Toeplitz matrix of order $5$: $$\begin{bmatrix} 1 & 2 & 0 & 0 & 0 \\ 2 & 1 & 2 & 0 & 0 \\ 0 & 2 & 1 & 2 & 0 \\ 0 & 0 & 2 & 1 & 2 \\ 0 & 0 & 0 & 2 & 1 \end{bmatrix}$$
- An example of a "filled" symmetric Toeplitz matrix of order $5$: $$\begin{bmatrix} 1 & 2 & 2 & 2 & 2 \\ 2 & 1 & 2 & 2 & 2 \\ 2 & 2 & 1 & 2 & 2 \\ 2 & 2 & 2 & 1 & 2 \\ 2 & 2 & 2 & 2 & 1 \end{bmatrix}$$

All the functions have to return the matrices as `NumPy`

's `matrix`

type, which is easily created from a list or any other two-dimensional array-like type, using the function `numpy.matrix`

. For example, the first of the three matrices shown above can be created as follows:

In [1]:

```
import numpy as np
L = list(range(1,6))
res = list()
for i in range(5):
row = list()
for j in range(5):
row.append(L[abs(i-j)])
res.append(row)
res = np.matrix(res)
print(res)
```

It can be done more easily using a generator expression:

In [2]:

```
import numpy as np
L = list(range(1,6))
res = np.matrix([[L[abs(i-j)] for i in range(5)] for j in range(5)])
print(res)
```

You may implement the functions `tridiagonal(n, d, sd)`

and `filled(n, d, nd)`

either on their own or simply by creating `L`

and calling `general(L)`

. The former approach is prefered, in which case `numpy.zeros`

, `numpy.ones`

, `numpy.fill_diagonal`

, and `numpy.tri`

may help you, along with the matrix addition (done with the usual plus `+`

operator) and multiplication with a constant (done with the usual multiplication `*`

operator). These functions are much faster than setting the elements index by index, but you are free to work without them.

**Problem 2.** Using the module `symtoep`

, write a program that loads a matrix (one of the 3 supported types), and then prints it and its eigenvalues.

To compute the eigenvalues of a symmetric matrix, use `scipy.linalg.eigvalsh`

. If, for some reason, SciPy doesn't work, use `numpy.linalg.eigvalsh`

.

Ideally, make your program run properly with either (but this is not a must). For example,

```
try:
import scipy.linalg as la
except ImportError:
import numpy.linalg as la
...
print(la.eigvalsh(A))
```

**Problem 3.** Using the module `symtoep`

, write a program that loads a matrix (one of the 3 supported types), and then prints it and a message explaining if it is positive semidefinite or not.

To test positive semidefiniteness, try to compute the Cholesky factorization, using either `scipy.linalg.cholesky`

or `numpy.linalg.cholesky`

. If the computation fails (i.e., a `numpy.linalg.linalg.LinAlgError`

exception is raised), the matrix is not positive semidefinite. Otherwise, it is.

The same remarks from Problem 2 regarding the import of SciPy/NumPy modules apply.