Diffusion Solver

Diffusion solvers are emerging as powerful tools for tackling complex computational problems, particularly in optimization and inverse problems, by leveraging the generative capabilities of diffusion models. Current research focuses on improving the efficiency and stability of these solvers, exploring various architectures like ODE and SDE-based methods, and incorporating techniques such as momentum and history gradient updates to enhance solution quality and speed. This approach offers significant potential for accelerating computations in diverse fields, ranging from network optimization and combinatorial problems to image reconstruction and motion generation, surpassing traditional methods in both speed and accuracy.