Make Optimization Once and for All with Fine-grained Guidance

Understand

Learning to Optimize (L2O) enhances optimization efficiency with integrated neural 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 specific optimization processes, 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.