English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, based on the limited memory techniques and subspaceminimization conjugate gradient (SMCG) methods, a regularized limited memorysubspace minimization conjugate gradient method is proposed, which contains twotypes of iterations. In SMCG iteration, we obtain the search direction byminimizing the approximate quadratic model or approximate regularization model.In RQN iteration, combined with regularization technique and BFGS method, amodified regularized quasi-Newton method is used in the subspace to improve theorthogonality. Moreover, some simple acceleration criteria and an improvedtactic for selecting the initial stepsize to enhance the efficiency of thealgorithm are designed. Additionally, an generalized nonmonotone line search isutilized and the global convergence of our proposed algorithm is establishedunder mild conditions. Finally, numerical results show that, the proposedalgorithm has a significant improvement over ASMCG_PR and is superior to theparticularly well-known limited memory conjugate gradient software packagesCG_DESCENT (6.8) and CGOPT(2.0) for the CUTEr library.

A Regularized Limited Memory Subspace Minimization Conjugate Gradient Method for Unconstrained Optimization