Greedy Algorithm

Greedy algorithms are iterative optimization methods that make locally optimal choices at each step, aiming to find a near-optimal solution without exhaustive search. Current research focuses on extending their application to complex problems like submodular maximization, sensor placement, and model pruning, often incorporating enhancements such as evolutionary strategies, differentiable relaxations, or biased selection to improve performance and address dynamic constraints. This ongoing work is significant because greedy algorithms offer computationally efficient solutions for large-scale problems across diverse fields, from resource allocation and machine learning to robotics and network optimization.