We design a well-developed algorithm for solving large bound-constrained systems. The method combines a subspace trust-region strategy with a Newton-Krylov method and with a strategy for handling the bounds. To solve the trust-region problem efficiently, we look for an approximate solution and do not require the exact solution of the Newton equation. In addition, a strategy for obtaining an imp...

Gaussian process (GP) regression is a Bayesian non-parametric regression model, showing good performance in various applications. However, it is quite rare to see research results on log-likelihood maximization algorithms. Instead of the commonly used conjugate gradient method, the Hessian matrix is first derived/simplified in this paper and the trust-region optimization method is then presente...