Yu-Chen Gao

Leveraging High-Performance Scientific Computing for the Limited-Memory Hyperparameter Tuning of Large-Scale Unconstrained Continuous Optimization Solvers

This work investigates a data-driven selection of the L-BFGS memory parameter (mem) for large-scale unconstrained optimization and proposes a two-stage hybrid machine-learning framework that predicts intermediate solver quantities and then forecasts runtime for each candidate memory value.