Explicit pseudo-transient continuation and the trust-region updating strategy for unconstrained optimization

TL;DR

This paper introduces a new explicit continuation method with a switching preconditioning strategy for unconstrained optimization, significantly improving computational efficiency and robustness over traditional trust-region and line search methods.

Contribution

The paper proposes a novel explicit pseudo-transient continuation method with a switching preconditioning technique, enhancing efficiency and robustness in unconstrained optimization.

Findings

The new method is more robust and faster than traditional methods.

Computational time is about 1% of trust-region and 20% of line search methods.

Global convergence of the method is established.

Abstract

This paper considers an explicit continuation method and the trust-region updating strategy for the unconstrained optimization problem. Moreover, in order to improve its computational efficiency and robustness, the new method uses the switching preconditioning technique. In the well-conditioned phase, the new method uses the L-BFGS method as the preconditioning technique in order to improve its computational efficiency. Otherwise, the new method uses the inverse of the Hessian matrix as the pre-conditioner in order to improve its robustness. Numerical results aslo show that the new method is more robust and faster than the traditional optimization method such as the trust-region method and the line search method. The computational time of the new method is about one percent of that of the trust-region method (the subroutine fminunc.m of the MATLAB2019a environment, it is set by the trust-region method) or one fifth of that the line search method (fminunc.m is set by the quasi-Newton method) for the large-scale problem. Finally, the global convergence analysis of the new method is also given.