On this webpage, we provide a curated list of computer science problems partitioned into several algorithm design approaches (techniques) such as divide & conquer, greedy approach, dynamic programming, and backtracking.

 We present each computer science problem on an interactive jupyter notebook page. Jupyter Notebook pages are hosted at Google Colab and include the following sections:

• Description of the computer science problem with sample inputs and output.

• Visualization of the problem

• Brute force solution and its time complexity analysis

• Steps for solving the problem using a design approach (technique)

• Pseudocode of the solution using the algorithmic design approach

• Executable Python code implementation for the design approach

• Asymptotic (Time and Space) Complexity Analysis of the Solution

• Application areas of the computer science problem

• References

A template of a Jupyter Notebook page that includes the sections listed above can be downloaded here.

We present interactive examples of algorithm design approaches in the following four main categories: Divide & Conquer, Greedy Approach, Dynamic Programming, and Backtracking. The following web pages include the description of the design technique, steps involved in applying the technique, and a list of jupyter notebooks that each presents how to solve a computer science problem using the technique.

A list of well-known computer science problems categorized under algorithm design techniques and their brief description can be found here.