[图书][B] Evaluation Complexity of Algorithms for Nonconvex Optimization: Theory, Computation and Perspectives
Do you know the difference between an optimist and a pessimist? The former believes we
live in the best possible world, and the latter is afraid that the former might be right.… In that …
live in the best possible world, and the latter is afraid that the former might be right.… In that …
Error estimates for iterative algorithms for minimizing regularized quadratic subproblems
NIM Gould, V Simoncini - Optimization Methods and Software, 2020 - Taylor & Francis
We derive bounds for the objective errors and gradient residuals when finding
approximations to the solution of common regularized quadratic optimization problems …
approximations to the solution of common regularized quadratic optimization problems …
Riemannian Adaptive Regularized Newton Methods with H\" older Continuous Hessians
This paper presents strong worst-case iteration and operation complexity guarantees for
Riemannian adaptive regularized Newton methods, a unified framework encompassing both …
Riemannian adaptive regularized Newton methods, a unified framework encompassing both …
On convergence of the generalized Lanczos trust-region method for trust-region subproblems
B Feng, G Wu - arXiv preprint arXiv:2207.12674, 2022 - arxiv.org
The generalized Lanczos trust-region (GLTR) method is one of the most popular
approaches for solving large-scale trust-region subproblem (TRS). Recently, Jia and Wang …
approaches for solving large-scale trust-region subproblem (TRS). Recently, Jia and Wang …