Optimization Problem

Optimization problems, aiming to find the best solution among many possibilities, are central to numerous scientific and engineering disciplines. Current research emphasizes developing efficient algorithms, including metaheuristics, neural networks leveraging Karush-Kuhn-Tucker conditions, and bilevel optimization approaches, to tackle increasingly complex problems, such as those with multiple objectives, constraints, and dynamic environments. These advancements are improving the speed and accuracy of solutions across diverse fields, from machine learning and resource allocation to traffic control and scientific discovery, enabling better decision-making and system design. Furthermore, research is actively exploring methods to enhance the explainability and fairness of optimization solutions.