Optimization Solver

Optimization solvers aim to find the best solution among many possibilities, a crucial task across diverse scientific and engineering fields. Current research emphasizes improving solver efficiency and robustness, particularly for complex, non-convex problems, through techniques like integrating machine learning models (e.g., neural networks, diffusion models) to provide better initial guesses or reduce problem dimensionality. These advancements are impacting various applications, from robotics and autonomous systems (e.g., trajectory optimization, motion planning) to resource allocation and Bayesian inference, by enabling faster and more reliable solutions to previously intractable problems.