A block upper triangular preconditioner for double saddle point problems arising from liquid crystal modeling

To address the double saddle point problems that emerge in liquid crystal modeling, this article proposes the utilization of the preconditioned generalized minimal residual method for their resolution. Initially, a block upper triangular (BUT) splitting of the coefficient matrix is introduced to facilitate this objective. Subsequently, the BUT preconditioner, incorporating relevant parameters, is logically derived. Furthermore, the convergence of the iteration method when employing the preconditioner is delved into in great detail. To better demonstrate the preconditioner’s efficacy, the formula for calculating the parameters has also been established. Ultimately, the numerical example confirms that the robustness of the proposed block preconditioner exceeds that of the four existing block preconditioners, especially in the case of large dimensions.

Authors

Institutions

Publication Details

Journal
AIP Advances
Published
2025-12-01
DOI
https://doi.org/10.1063/5.0303109
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A block upper triangular preconditioner for double saddle point problems arising from liquid crystal modeling

Jun Li, Liqiang Feng
AIP Advances
Matrix Theory and Algorithms
article

A block upper triangular preconditioner for double saddle point problems arising from liquid crystal modeling

Jun Li, Liqiang Feng
article en

Abstract

To address the double saddle point problems that emerge in liquid crystal modeling, this article proposes the utilization of the preconditioned generalized minimal residual method for their resolution. Initially, a block upper triangular (BUT) splitting of the coefficient matrix is introduced to facilitate this objective. Subsequently, the BUT preconditioner, incorporating relevant parameters, is logically derived. Furthermore, the convergence of the iteration method when employing the preconditioner is delved into in great detail. To better demonstrate the preconditioner’s efficacy, the formula for calculating the parameters has also been established. Ultimately, the numerical example confirms that the robustness of the proposed block preconditioner exceeds that of the four existing block preconditioners, especially in the case of large dimensions.

AIP AdvancesVol. 15(12)
Lanzhou University of Technology (CN), Northwest Normal University (CN)
Openalex Percentile: Top 61%
Matrix Theory and Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A block upper triangular preconditioner for double saddle point problems arising from liquid crystal modeling — Jun Li, Liqiang Feng · AIP Advances (2025) | TGRS Research Map | TGRS