Optimization Method

Optimization methods seek to find the best solution among many possibilities, a fundamental problem across diverse scientific and engineering fields. Current research emphasizes developing efficient algorithms, including Bayesian optimization, evolutionary strategies, and gradient-based methods tailored to specific problem structures (e.g., handling high dimensionality, non-convexity, or constraints), often integrating them with machine learning techniques like deep learning and reinforcement learning. These advancements improve the speed and accuracy of solving complex optimization problems, impacting areas such as materials design, robotics, medical imaging, and power systems management.