Explicit Optimization

Explicit optimization focuses on directly manipulating parameters within optimization algorithms to achieve desired outcomes, contrasting with implicit methods that modify the optimization landscape indirectly. Current research explores explicit optimization across diverse applications, including molecular design (using geometric diffusion models), self-supervised learning (leveraging mutual information maximization), and dynamic environments (employing adaptive metaheuristics). These advancements aim to improve efficiency, interpretability, and robustness in optimization processes, impacting fields ranging from materials science and drug discovery to machine learning and signal processing.