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T-414-ÁFLV: A Competitive Programming Course

I held a course about Competitive Programming at Reykjavik University on the fall semester of 2014. It was three-week long with a fresh lecture and problem set each day. There were almost 90 students registered and it turned out to be a big success, although some students complained about heavy workload. For me it was a very rewarding experience.

Below you may find the lecture slides (including LaTeX sources), the problem sets, and other supporting material. All problems are available on the Open Kattis online judge.

Note: The course material is now accessible on Github. Contributions are welcome.
Lecture 1: Introduction

Covers some basic things about the course, and then introduces competitive programming.

Material

Problems

Solve some of the following problems on Kattis. You need 5 points to get full score.

Bonus problems

If you want a challenge, you can try solving the following bonus problems.

Lecture 2: Data structures and libraries

Reviews the most basic data types and data structures. Covers how to represent big integers, sets and graphs, and how to augment binary search trees.

Material

Problems

Solve some of the following problems on Kattis. You need 5 points to get full score.

Bonus problems

If you want a challenge, you can try solving the following bonus problems.

Lecture 3: Data structures

Reviews the Union-Find disjoint sets data structure, and covers range queries and Segment Trees.

Material

Problems

Solve some of the following problems on Kattis. You need 3 points to get full score.

Bonus problems

If you want a challenge, you can try solving the following bonus problems.

Lecture 4: Problem solving paradigms

Introduces problem solving paradigms, and covers complete search, backtracking, and divide & conquer. Covers binary search, and binary exponentiation.

Material

Problems

Solve some of the following problems on Kattis. You need 3 points to get full score.

Bonus problems

If you want a challenge, you can try solving the following bonus problems.

Lecture 5: Greedy algorithms

Introduces greedy algorithms, and covers coin changing, interval scheduling, and scheduling to minimize lateness.

Material

  • Lecture slides: PDF

Problems

Solve some of the following problems on Kattis. You need 3 points to get full score.

Bonus problems

If you want a challenge, you can try solving the following bonus problems.

Problem session 1

Teams of up to three students had to solve the following problems. They had three hours, and were only allowed to use a single computer.

Lecture 6: Dynamic programming

Introduces dynamic programming and goes over some examples.

Material

Problems

Solve some of the following problems on Kattis. You need 3 points to get full score.

Bonus problems

If you want a challenge, you can try solving the following bonus problems.

Lecture 7: Mathematics

Covers some basic topics in mathematics, including number theory, combinatorics and game theory.

Material

Problems

Solve some of the following problems on Kattis. You need 3 points to get full score.

Bonus problems

If you want a challenge, you can try solving the following bonus problems.

Lecture 8: Unweighted graphs

Reviews basic graph theory. Covers depth-first search, breadth-first search, and applications.

Material

Problems

Solve some of the following problems on Kattis. You need 3 points to get full score.

Bonus problems

If you want a challenge, you can try solving the following bonus problems.

Lecture 9: Graphs

Covers minimum spanning trees, shortest paths, some graph theory, and some special types of graphs.

Material

Problems

Solve some of the following problems on Kattis. You need 3 points to get full score.

Bonus problems

If you want a challenge, you can try solving the following bonus problems.

Lecture 10: Network flow

Covers maximum flow, minimum cut, bipartite matching, and other applications.

Material

Problems

Solve some of the following problems on Kattis. You need 3 points to get full score.

Bonus problems

If you want a challenge, you can try solving the following bonus problems.

Problem session 2

Teams of up to three students had to solve the following problems. They had three hours, and were only allowed to use a single computer.

Lecture 11: Strings

Covers string matching, KMP, tries, suffix arrays, and applications.

Material

Problems

Solve some of the following problems on Kattis. You need 3 points to get full score.

Bonus problems

If you want a challenge, you can try solving the following bonus problems.

Lecture 12: Geometry

Covers basic geometry, convex hulls, area of polygons, point in polygon, and closest pair of points.

Material

Problems

Solve some of the following problems on Kattis. You need 3 points to get full score.

Bonus problems

If you want a challenge, you can try solving the following bonus problems.

Final exam

At the end of the course there was a four-hour individual final exam. It was as follows.

Part 1 (30%)

Solve one of the following problems.

Part 2 (20%)

Solve one of the following problems.

Part 3 (20%)

Solve one of the following problems.

Part 4 (20%)

Solve one of the following problems.

Part 5 (20%)

Solve one of the following problems.

Part 6 (20%) (Bonus)

Solve one of the following problems.

CC BY 4.0 This work is licensed under a Creative Commons Attribution 4.0 International License.

Published inCompetitive Programming

14 Comments

  1. Mohammad Yasser Mohammad Yasser

    Great work. Thanks!

  2. Joaquin Joaquin

    Great Job Guys!. It’s really useful for beginners like me :).

  3. Muhammad Attia Muhammad Attia

    Awesome course .

  4. MATH MATH

    Hi,
    Great job, there is just a typo in the lecture on Data structures and lib (lecture 2). Slide 32, you added an edge connecting a vetrex to itself. “adj[2].push_back(2);” . Tiny problem, but worth fixing.
    -MATH
    p.s. this is a 10 minute email account.

  5. Zabir Al Nazi Nabil Zabir Al Nazi Nabil

    Great materials you got there , Thanks

  6. Sandip Kumar Sandip Kumar

    Great collection sir!!!

  7. unknown unknown

    In your Data Structures lesson specifically Disjoint Sets, you don’t implement union by rank you only apply path compression, this is not optimal. Both are fairly straightforward heuristics and by using them you can achieve optimal efficiency.

    Regards

    • unknown unknown

      This observation also happens in the 2016 course.

  8. Divya Rani Divya Rani

    Thank you so much for the materials! I have no one to guide me and these materials really helped me a lot.

  9. Pravesh Pravesh

    Hi,
    Thanks for the resources.

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