Materials for the practicum for "Fundamentals of Algorithms" course at SpbU

Set up your environment

Go to Run and Debug in the left panel, create a new launch file, select Python File and add the following field:

"env": {
    "PYTHONPATH": "${workspaceFolder}${pathSeparator}${env:PYTHONPATH}"
}

We study basic tools necessary for the rest of the course: python, numpy and matplotlib. It is assumed that a student has some decent knowledge of python though he/she is not very experienced in it.

Plan:

1. Warm-up
2. Go through intro_to_numpy_and_matplotlib.ipynb together

We start working on graph algorithms via introducing networkx and then a couple of simple algorithm for graph traversals.

Plan:

1. Warm-up
2. Go through intro_to_networkx.ipynb together
3. Complete bfs_maze_template.py
4. Go through dfs_recursive() in dfs_maze.py together
5. Complete dfs_iterative() in dfs_maze_template.py
6. Complete topological_sort() in dfs_maze_template.py
7. Go through dfs_recursive_postorder() in dfs_maze.py together (solution for point 6)

We study two classical graph problems: Minimum Spanning Tree and Shortest Path. We use Prim's algorithm to solve the former and Dijkstra's algorithm to solve the latter.

Plan:

1. Warm-up
2. Complete mst_template.py
3. Complete sp_template.py. We can do both the original version and the version with a priority queue.

We study fundamental data structures.

Plan:

1. Warm-up
2. Complete valid_parentheses.py (LIFO)
3. Complete time_needed_to_buy_tickets.py (FIFO)
4. Complete linked_list.py (linked list)

Homework:

1. time_needed_to_buy_tickets.py: implement a proper solution for this problem.

We study simple computaional geomtery algorithms such as convex hull computing.

Plan:

1. Warm-up
2. Complete slow_convex_hull.py
3. Complete qwer

Homework:

1. convex_bucket.py: implement a convex hull algorithm constructing only the lower part of a convex hull which would "hold" all the points if they fell due to the gravity.

Cubic spline: http://getsomemath.ru/subtopic/computational_mathematics/approximation_theory/local_interpolation

LU: http://getsomemath.ru/subtopic/computational_mathematics/numerical_linear_algebra/gauss_methods