Title: Make Optimization Once and for All with Fine-grained Guidance
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Optimization; Diffusion; Learning to Optimize

Abstract: Learning to Optimize (L2O) enhances optimization efficiency with integrated neu- ral networks. L2O paradigms achieve great outcomes, e.g., refitting optimizer, generating unseen solutions iteratively or directly. However, conventional L2O methods require intricate design and rely on real optimization processes and nu- merical optimization results, limiting scalability and generalization. Our analyses explore general framework for learning optimization, called Diff-L2O, focusing on augmenting sampled solutions from a wider view rather than local updates in real optimization process only. Meanwhile, we give the related generalization bound, showing that the sample diversity of Diff-L2O brings better performance. This bound can be simply applied to other fields, discussing diversity, mean-variance, and different tasks. Diff-L2O’s strong compatibility is empirically verified with only minute-level training, comparing with other hour-levels.

TL;DR: We propose fine-grained modeling on solution space. Takeaways: 1) Optimization process’s meta features do provide informa- tion for solution space modeling. 2) The data from real optimization process is helpful, but it is still not enough.