Skip to main content
Algorithmic techniques and tricks are essential problem-solving strategies that help you write efficient code and tackle complex challenges. This section covers fundamental approaches used across computer science.

Recursion

Function calling itself to solve problems by breaking them into smaller instances

Backtracking

Exploring all possible solutions and abandoning paths that don’t work

Dynamic programming

Optimizing recursive solutions by storing and reusing previously computed results

Gauss sum

Mathematical formula for quickly summing consecutive integers

​
When to use these techniques

Use recursion when a problem can be broken down into smaller, similar subproblems. Common use cases include tree traversal, factorial calculation, and divide-and-conquer algorithms.
Use backtracking for constraint satisfaction problems where you need to explore all possible solutions, such as puzzle solving, pathfinding, and combinatorial optimization.
Use dynamic programming when you have overlapping subproblems and optimal substructure. Common applications include fibonacci sequences, shortest paths, and optimization problems.
Use the Gauss sum formula when you need to quickly calculate the sum of consecutive integers without iterating through them.

Build docs developers (and LLMs) love

Get started for freeTalk to us