Algorithmic implementations for self-contained and distinct mini-problems. From basic algorithms and data structures like sorting or linked list to domain specific ones like Hybrid A*.

NP-hard or NP-complete problems that non-exponential solutions is not possible for general cases. What we can do includes find feasible solutions for special or common cases, try approximation as opposed to optimal solutions.

More than one approach for each of the problems.

For now it has the following problems

1. 0-1 knapsack
2. graph coloring
3. traveling salesman problem

Documentations are available under the subdirectory, including papers from which my code implementation employed various techniques.

Prototypes or simplified algorithmic applications that serve as demonstrations.

For now it has the following algorithms

1. Gaussian naive Bayes
2. Perceptron learning algorithm and its variant pocket learning algorithm.

Documentations are available under the subdirectory.