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A second-level diagonal preconditionerfor single-step SNPBLUP. / Vandenplas, Jeremie; Calus, Mario P.L.; Eding, Herwin; Vuik, Cornelis.

In: Genetics Selection Evolution, Vol. 51, 30, 2019, p. 1-16.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Vandenplas, J, Calus, MPL, Eding, H & Vuik, C 2019, 'A second-level diagonal preconditionerfor single-step SNPBLUP' Genetics Selection Evolution, vol. 51, 30, pp. 1-16. https://doi.org/10.1186/s12711-019-0472-8

APA

Vandenplas, J., Calus, M. P. L., Eding, H., & Vuik, C. (2019). A second-level diagonal preconditionerfor single-step SNPBLUP. Genetics Selection Evolution, 51, 1-16. [30]. https://doi.org/10.1186/s12711-019-0472-8

Vancouver

Vandenplas J, Calus MPL, Eding H, Vuik C. A second-level diagonal preconditionerfor single-step SNPBLUP. Genetics Selection Evolution. 2019;51:1-16. 30. https://doi.org/10.1186/s12711-019-0472-8

Author

Vandenplas, Jeremie ; Calus, Mario P.L. ; Eding, Herwin ; Vuik, Cornelis. / A second-level diagonal preconditionerfor single-step SNPBLUP. In: Genetics Selection Evolution. 2019 ; Vol. 51. pp. 1-16.

BibTeX

@article{981070e484c44d29ba26286556653d3b,
title = "A second-level diagonal preconditionerfor single-step SNPBLUP",
abstract = "BackgroundThe preconditioned conjugate gradient (PCG) method is an iterative solver of linear equations systems commonly used in animal breeding. However, the PCG method has been shown to encounter convergence issues when applied to single-step single nucleotide polymorphism BLUP (ssSNPBLUP) models. Recently, we proposed a deflated PCG (DPCG) method for solving ssSNPBLUP efficiently. The DPCG method introduces a second-level preconditioner that annihilates the effect of the largest unfavourable eigenvalues of the ssSNPBLUP preconditioned coefficient matrix on the convergence of the iterative solver. While it solves the convergence issues of ssSNPBLUP, the DPCG method requires substantial additional computations, in comparison to the PCG method. Accordingly, the aim of this study was to develop a second-level preconditioner that decreases the largest eigenvalues of the ssSNPBLUP preconditioned coefficient matrix at a lower cost than the DPCG method, in addition to comparing its performance to the (D)PCG methods applied to two different ssSNPBLUP models.ResultsBased on the properties of the ssSNPBLUP preconditioned coefficient matrix, we proposed a second-level diagonal preconditioner that decreases the largest eigenvalues of the ssSNPBLUP preconditioned coefficient matrix under some conditions. This proposed second-level preconditioner is easy to implement in current software and does not result in additional computing costs as it can be combined with the commonly used (block-)diagonal preconditioner. Tested on two different datasets and with two different ssSNPBLUP models, the second-level diagonal preconditioner led to a decrease of the largest eigenvalues and the condition number of the preconditioned coefficient matrices. It resulted in an improvement of the convergence pattern of the iterative solver. For the largest dataset, the convergence of the PCG method with the proposed second-level diagonal preconditioner was slower than the DPCG method, but it performed better than the DPCG method in terms of total computing time.ConclusionsThe proposed second-level diagonal preconditioner can improve the convergence of the (D)PCG methods applied to two ssSNPBLUP models. Based on our results, the PCG method combined with the proposed second-level diagonal preconditioner seems to be more efficient than the DPCG method in solving ssSNPBLUP. However, the optimal combination of ssSNPBLUP and solver will most likely be situation-dependent.",
author = "Jeremie Vandenplas and Calus, {Mario P.L.} and Herwin Eding and Cornelis Vuik",
note = "green",
year = "2019",
doi = "10.1186/s12711-019-0472-8",
language = "English",
volume = "51",
pages = "1--16",
journal = "Genetics Selection Evolution",
issn = "1297-9686",
publisher = "BioMed Central",

}

RIS

TY - JOUR

T1 - A second-level diagonal preconditionerfor single-step SNPBLUP

AU - Vandenplas, Jeremie

AU - Calus, Mario P.L.

AU - Eding, Herwin

AU - Vuik, Cornelis

N1 - green

PY - 2019

Y1 - 2019

N2 - BackgroundThe preconditioned conjugate gradient (PCG) method is an iterative solver of linear equations systems commonly used in animal breeding. However, the PCG method has been shown to encounter convergence issues when applied to single-step single nucleotide polymorphism BLUP (ssSNPBLUP) models. Recently, we proposed a deflated PCG (DPCG) method for solving ssSNPBLUP efficiently. The DPCG method introduces a second-level preconditioner that annihilates the effect of the largest unfavourable eigenvalues of the ssSNPBLUP preconditioned coefficient matrix on the convergence of the iterative solver. While it solves the convergence issues of ssSNPBLUP, the DPCG method requires substantial additional computations, in comparison to the PCG method. Accordingly, the aim of this study was to develop a second-level preconditioner that decreases the largest eigenvalues of the ssSNPBLUP preconditioned coefficient matrix at a lower cost than the DPCG method, in addition to comparing its performance to the (D)PCG methods applied to two different ssSNPBLUP models.ResultsBased on the properties of the ssSNPBLUP preconditioned coefficient matrix, we proposed a second-level diagonal preconditioner that decreases the largest eigenvalues of the ssSNPBLUP preconditioned coefficient matrix under some conditions. This proposed second-level preconditioner is easy to implement in current software and does not result in additional computing costs as it can be combined with the commonly used (block-)diagonal preconditioner. Tested on two different datasets and with two different ssSNPBLUP models, the second-level diagonal preconditioner led to a decrease of the largest eigenvalues and the condition number of the preconditioned coefficient matrices. It resulted in an improvement of the convergence pattern of the iterative solver. For the largest dataset, the convergence of the PCG method with the proposed second-level diagonal preconditioner was slower than the DPCG method, but it performed better than the DPCG method in terms of total computing time.ConclusionsThe proposed second-level diagonal preconditioner can improve the convergence of the (D)PCG methods applied to two ssSNPBLUP models. Based on our results, the PCG method combined with the proposed second-level diagonal preconditioner seems to be more efficient than the DPCG method in solving ssSNPBLUP. However, the optimal combination of ssSNPBLUP and solver will most likely be situation-dependent.

AB - BackgroundThe preconditioned conjugate gradient (PCG) method is an iterative solver of linear equations systems commonly used in animal breeding. However, the PCG method has been shown to encounter convergence issues when applied to single-step single nucleotide polymorphism BLUP (ssSNPBLUP) models. Recently, we proposed a deflated PCG (DPCG) method for solving ssSNPBLUP efficiently. The DPCG method introduces a second-level preconditioner that annihilates the effect of the largest unfavourable eigenvalues of the ssSNPBLUP preconditioned coefficient matrix on the convergence of the iterative solver. While it solves the convergence issues of ssSNPBLUP, the DPCG method requires substantial additional computations, in comparison to the PCG method. Accordingly, the aim of this study was to develop a second-level preconditioner that decreases the largest eigenvalues of the ssSNPBLUP preconditioned coefficient matrix at a lower cost than the DPCG method, in addition to comparing its performance to the (D)PCG methods applied to two different ssSNPBLUP models.ResultsBased on the properties of the ssSNPBLUP preconditioned coefficient matrix, we proposed a second-level diagonal preconditioner that decreases the largest eigenvalues of the ssSNPBLUP preconditioned coefficient matrix under some conditions. This proposed second-level preconditioner is easy to implement in current software and does not result in additional computing costs as it can be combined with the commonly used (block-)diagonal preconditioner. Tested on two different datasets and with two different ssSNPBLUP models, the second-level diagonal preconditioner led to a decrease of the largest eigenvalues and the condition number of the preconditioned coefficient matrices. It resulted in an improvement of the convergence pattern of the iterative solver. For the largest dataset, the convergence of the PCG method with the proposed second-level diagonal preconditioner was slower than the DPCG method, but it performed better than the DPCG method in terms of total computing time.ConclusionsThe proposed second-level diagonal preconditioner can improve the convergence of the (D)PCG methods applied to two ssSNPBLUP models. Based on our results, the PCG method combined with the proposed second-level diagonal preconditioner seems to be more efficient than the DPCG method in solving ssSNPBLUP. However, the optimal combination of ssSNPBLUP and solver will most likely be situation-dependent.

U2 - 10.1186/s12711-019-0472-8

DO - 10.1186/s12711-019-0472-8

M3 - Article

VL - 51

SP - 1

EP - 16

JO - Genetics Selection Evolution

T2 - Genetics Selection Evolution

JF - Genetics Selection Evolution

SN - 1297-9686

M1 - 30

ER -

ID: 54898194