GLENS

Complexity bounds for learning projected gradient descent solver iterates via k-neighborhood data augmentation

A new arXiv preprint derives Rademacher complexity-based generalization bounds for learning to predict intermediate iterates of projected gradient descent solvers applied to box-constrained quadratic programs. The authors propose a k-neighborhood data collection strategy that augments converged-solution datasets with intermediate solver states, increasing training data without additional solver runs. The work connects to GLENS, a data-efficient global search method, and frames the approach within the Dynamic Data-Driven Application Systems (DDDAS) paradigm for tightening data-model-optimization loops.