Global Solution

Finding global solutions to complex, often non-convex, problems is a central challenge across diverse scientific fields. Current research focuses on developing novel algorithms, such as those leveraging neural networks, potential game theory, and modified gradient descent methods, to guarantee or improve convergence to global optima in various contexts, including macroeconomic modeling, continual learning, and sensor network localization. These advancements are crucial for improving the accuracy and reliability of models in numerous applications, ranging from economic forecasting and machine learning to physical simulations and data analysis. The ultimate goal is to move beyond local solutions and achieve a more complete and accurate understanding of complex systems.