Implement approximation algorithms

Create implementations for approximation algorithms:

## Fundamental approximation algorithms:
- Vertex cover approximation
- Set cover approximation
- Traveling salesman problem approximations
- Knapsack problem approximation
- Bin packing approximation
- Maximum cut approximation
- k-center clustering approximation

## Techniques to implement:
- Greedy approximation strategies
- Linear programming relaxation
- Randomized rounding
- Local search methods
- Primal-dual method

## Requirements:
- Provide approximation ratio analysis
- Include proof of approximation bounds
- Create test cases comparing to exact solutions
- Document trade-offs between quality and speed

This collection will demonstrate approaches for efficiently finding near-optimal solutions to NP-hard problems where exact solutions are computationally infeasible.