Classical Optimization

Classical optimization focuses on finding the best solution to mathematical problems, often involving iterative algorithms to minimize or maximize objective functions. Current research emphasizes developing more efficient and robust optimization methods, including exploring the synergy between classical approaches and machine learning techniques like neural networks and reinforcement learning, particularly for complex problems with high dimensionality or non-convexity. These advancements are impacting diverse fields, from medical image analysis and power systems management to quantum computing and portfolio optimization, by enabling faster and more accurate solutions to previously intractable problems.