Solves NP-hard optimization problems using greedy construction and iterative local improvement patterns.
This skill provides a structured framework for tackling complex NP-hard optimization problems such as the Traveling Salesman Problem (TSP), job scheduling, and resource assignment. By leveraging algorithmic patterns from Peter Norvig's 'pytudes,' it combines fast greedy construction with iterative local improvement techniques like Hill Climbing and 2-opt. This approach allows Claude to generate high-quality, 'good enough' solutions for computationally expensive problems where finding a global optimum via exact methods would be too slow or resource-intensive.
주요 기능
01Neighborhood definition and move selection strategies
021 GitHub stars
03Hill climbing implementation for iterative local improvement
04Greedy construction patterns for rapid initial solutions
05Multiple restart logic to mitigate local optima traps
062-opt optimization specifically for route and TSP problems
사용 사례
01Solving facility location and resource allocation constraints
02Automating complex job shop and task scheduling
03Optimizing delivery routes and vehicle routing problems
