Greedy Random Start Algorithms: From TSP to Daily Life

This 16-minute video from Pragmatic AI Labs explores greedy random start algorithms, beginning with the Traveling Salesman Problem (TSP) and extending to practical applications in daily life. Learn about computational complexity classifications from constant time O(1) to factorial time O(n!), and understand the distinction between polynomial and non-polynomial time problems. Discover why the TSP is computationally intractable as the number of cities increases, with 50 cities creating more possible routes than atoms in the observable universe. Examine how the standard greedy approach works with O(n²) time complexity but suffers from getting trapped in local optima, and how random restart enhancement improves performance by sampling different regions of the solution space. The video concludes with real-world applications in urban navigation, traffic optimization, economic decision-making, and job search strategies, demonstrating how these algorithmic concepts apply beyond theoretical computer science.