  Gradient-based hyperparameter optimization methods update hyperparameters
using hypergradients, gradients of a meta criterion with respect to
hyperparameters. Previous research used two distinct update strategies:
optimizing hyperparameters using global hypergradients obtained after
completing model training or local hypergradients derived after every few model
updates. While global hypergradients offer reliability, their computational
cost is significant; conversely, local hypergradients provide speed but are
often suboptimal. In this paper, we propose *glocal* hypergradient estimation,
blending "global" quality with "local" efficiency. To this end, we use the
Koopman operator theory to linearize the dynamics of hypergradients so that the
global hypergradients can be efficiently approximated only by using a
trajectory of local hypergradients. Consequently, we can optimize
hyperparameters greedily using estimated global hypergradients, achieving both
reliability and efficiency simultaneously. Through numerical experiments of
hyperparameter optimization, including optimization of optimizers, we
demonstrate the effectiveness of the glocal hypergradient estimation.
