You are viewing the site in preview mode

Skip to main content

Table 1 Characteristics of preconditioned (deflated) coefficient matrices, and of PCG and DPCG methods for solving ssSNPBLUP applied to the reduced dataset

From: A second-level diagonal preconditioner for single-step SNPBLUP

\(\text{Model}^{\mathrm{a}}\) \(\text{Method}^{\mathrm{b}}\) \(k_{O}^{\mathrm{c}}\) \(k_{S}^{\mathrm{c}}\) \(k_{O}/k_{S}\) \(\lambda _{min}^{\mathrm{d}}\) \(\lambda _{max}^{\mathrm{d}}\) \(\kappa ^{\mathrm{e}}\) \(\textit{N}^{\mathrm{f}}\)
MS PCG 1 1 1 \(1.07\times 10^{-04}\) \(1.81\times 10^{2}\) \(1.70\times 10^{6}\) 1499
MS PCG 1 2 0.5 \(1.07\times 10^{-04}\) \(9.11\times 10^{1}\) \(8.55\times 10^{5}\) 1103
MS PCG 1 3.3 0.3 \(1.07\times 10^{-04}\) \(5.51\times 10^{1}\) \(5.17\times 10^{5}\) 862
MS PCG 1 \(10^{1}\) \(10^{-1}\) \(1.07\times 10^{-04}\) \(1.91\times 10^{1}\) \(1.79\times 10^{5}\) 560
MS PCG 1 \(10^{2}\) \(10^{-2}\) \(1.07\times 10^{-04}\) \(1.19\times 10^{1}\) \(1.12\times 10^{5}\) 417
MS PCG 1 \(10^{3}\) \(10^{-3}\) \(1.06\times 10^{-04}\) \(1.19\times 10^{1}\) \(1.12\times 10^{5}\) 608
MS PCG 1 \(10^{4}\) \(10^{-4}\) \(4.86\times 10^{-05}\) \(1.19\times 10^{1}\) \(2.45\times 10^{5}\) 1254
MS PCG 1 \(10^{5}\) \(10^{-5}\) \(4.87\times 10^{-06}\) \(1.19\times 10^{1}\) \(2.45\times 10^{6}\) 2350
MS PCG \(10^{-1}\) 1 \(10^{-1}\) \(1.07\times 10^{-03}\) \(1.91\times 10^{2}\) \(1.79\times 10^{5}\) 557
MS PCG \(10^{-2}\) 1 \(10^{-2}\) \(1.07\times 10^{-02}\) \(1.19\times 10^{3}\) \(1.12\times 10^{5}\) 416
MS PCG \(10^{-3}\) 1 \(10^{-3}\) \(1.06\times 10^{-01}\) \(1.19\times 10^{4}\) \(1.12\times 10^{5}\) 606
MS PCG \(10^{-4}\) 1 \(10^{-4}\) \(4.86\times 10^{-01}\) \(1.19\times 10^{5}\) \(2.45\times 10^{5}\) 1254
MS PCG \(10^{-5}\) 1 \(10^{-5}\) \(4.86\times 10^{-01}\) \(1.19\times 10^{6}\) \(2.45\times 10^{6}\) 2367
MS DPCG (1) 1 1 1 \(1.09\times 10^{-04}\) 6.44 \(5.93\times 10^{4}\) 294
MS DPCG (1) 1 \(10^{5}\) \(10^{-5}\) \(1.09\times 10^{-04}\) 6.44 \(5.92\times 10^{4}\) 293
MS DPCG (5) 1 1 1 \(1.07\times 10^{-04}\) 6.44 \(6.03\times 10^{4}\) 342
MS DPCG (5) 1 \(10^{1}\) \(10^{-1}\) \(1.07\times 10^{-04}\) 6.44 \(6.03\times 10^{4}\) 331
MS DPCG (5) 1 \(10^{2}\) \(10^{-2}\) \(1.07\times 10^{-04}\) 6.44 \(6.04\times 10^{4}\) 385
MS DPCG (5) 1 \(10^{3}\) \(10^{-3}\) \(1.06\times 10^{-04}\) 6.44 \(6.05\times 10^{4}\) 544
MS DPCG (5) 1 \(10^{4}\) \(10^{-4}\) \(4.96\times 10^{-05}\) 6.44 \(1.30\times 10^{5}\) 961
MS DPCG (5) 1 \(10^{5}\) \(10^{-5}\) \(4.95\times 10^{-06}\) 6.44 \(1.30\times 10^{6}\) 1456
Liu PCG 1 1 1 \(1.06\times 10^{-04}\) \(6.98\times 10^{1}\) \(6.56\times 10^{5}\) 1401
Liu PCG 1 \(10^{1}\) \(10^{-1}\) \(1.06\times 10^{-04}\) \(1.19\times 10^{1}\) \(1.12\times 10^{5}\) 561
Liu PCG 1 \(10^{2}\) \(10^{-2}\) \(1.06\times 10^{-04}\) \(1.19\times 10^{1}\) \(1.12\times 10^{5}\) 563
Liu PCG 1 \(10^{3}\) \(10^{-3}\) \(5.91\times 10^{-05}\) \(1.19\times 10^{1}\) \(2.02\times 10^{5}\) 1154
Liu DPCG (5) 1 1 1 \(1.07\times 10^{-04}\) 6.44 \(6.05\times 10^{4}\) 419
Liu DPCG (5) 1 \(10^{1}\) \(10^{-1}\) \(1.07\times 10^{-04}\) 6.44 \(6.05\times 10^{4}\) 399
Liu DPCG (5) 1 \(10^{2}\) \(10^{-2}\) \(1.06\times 10^{-04}\) 6.44 \(6.05\times 10^{4}\) 520
Liu DPCG (5) 1 \(10^{3}\) \(10^{-3}\) \(6.02\times 10^{-05}\) 6.44 \(1.07\times 10^{5}\) 1046
  1. \({}^{\mathrm{a}}\)MS = ssSNPBLUP model proposed by Mantysaari and Stranden [7]; Liu = ssSNPBLUP model proposed by Liu et al. [5]
  2. \({}^{\mathrm{b}}\)Number of SNP effects per subdomain is within brackets
  3. \({}^{\mathrm{c}}\)Parameters used for the second-level preconditioner \({\mathbf{D}}\)
  4. \({}^{\mathrm{d}}\)Smallest and largest eigenvalues of the preconditioned (deflated) coefficient matrix
  5. \({}^{\mathrm{e}}\)Condition number of the preconditioned (deflated) coefficient matrix
  6. \({}^{\mathrm{f}}\)Number of iterations. A number of iterations equal to 10,000 means that the method failed to converge within 10,000 iterations