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Table 4 Characteristics of preconditioned (deflated) coefficient matrices, and of PCG and DPCG methods for the field dataset

From: Deflated preconditioned conjugate gradient method for solving single-step BLUP models efficiently

Model Methoda Smallest eigenvalue Largest eigenvalue Effective condition number Number of iterationsb Total timec Time/iterationd
ssGBLUP PCG 2.3 × 10−5 5.1 2.2 × 105 729 3993 5.3 (0.4)
ssSNPBLUP PCG 3.7 × 10−5 1751.9 4.7 × 107 10,000 52,683 4.4 (0.4)
  DPCG (200) 1.2 × 10−5 193.1 1.6 × 107 10,000 92,171 9.2 (1.4)
  DPCG (50) 8.7 × 10−6 29.9 3.4 × 106 6074 52,503 8.6 (2.4)
  DPCG (5) 2.9 × 10−5 4.8 1.7 × 105 748 7735 8.7 (0.3)
ssPCBLUPe PCG 1.2 × 10−5 220.0 1.8 × 107 10,000 30,198 2.9 (0.2)
  DPCG (200) 8.3 × 10−6 113.3 1.4 × 107 10,000 58,280 5.8 (0.7)
  DPCG (50) 7.7 × 10−6 46.0 6.0 × 106 8541 55,388 6.5 (0.5)
  DPCG (5) 8.0 × 10−6 5.1 6.4 × 105 2686 15,063 5.6 (0.2)
  DPCG (1) 9.6 × 10−4 4.8 4.9 × 104 375 2402 6.3 (0.2)
  1. aNumber of SNP effects per subdomain is within brackets
  2. bA number of iterations equal to 10,000 means that the method failed to converge within 10,000 iterations
  3. cWall clock time (s) for the iterative process
  4. dAverage wall clock time (s) (SD within brackets) per iteration. Iterations computing the residual from the coefficient matrix for the PCG method were removed before averaging
  5. eThe number of principal components retained was equal to 13,803
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