An adaptive trust-region method without function evaluations

Nunes Grapiglia, Geovani;Stella, Gabriel F. D.
(2022) Computational Optimization and Applications : an international journal — Vol. 82, n° 1, p. 31-60 (2022)

Files

GS_2022.pdf
  • Open Access
  • Adobe PDF
  • 2.93 MB
Download

Details

Authors
Abstract
In this paper we propose an adaptive trust-region method for smooth unconstrained optimization. The update rule for the trust-region radius relies only on gradient evaluations. Assuming that the gradient of the objective function is Lipschitz continuous, we establish worst-case complexity bounds for the number of gradient evaluations required by the proposed method to generate approximate stationary points. As a corollary, we establish a global convergence result. We also present numerical results on benchmark problems. In terms of the number of calls of the oracle, the proposed method compares favorably with trust-region methods that use evaluations of the objective function.
Affiliations

Citations

  • APA
  • Chicago
  • FWB

Nunes Grapiglia, G., & Stella, G. F. D. (2022). An adaptive trust-region method without function evaluations. Computational Optimization and Applications : an international journal, 82(1), 31-60. https://doi.org/10.1007/s10589-022-00356-0 (Original work published 2022)