Elimination Based Algorithm

Elimination-based algorithms are a class of methods designed to efficiently identify the optimal solution or best option from a large search space, often by iteratively removing inferior candidates. Current research focuses on developing and analyzing these algorithms within various contexts, including solving systems of equations, statistical learning (e.g., curriculum learning and bandit problems), and real-world applications like real estate prediction and smart order routing. These algorithms offer significant advantages in terms of computational efficiency and robustness, particularly when dealing with complex problems or high-dimensional data, impacting fields ranging from machine learning to engineering.