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## ------- and still is less accurate here than the gradient-only version
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## ------- and still is less accurate here than the gradient-only version
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## all converge here, too,  fgh now being best
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## all converge here, too,  fgh now being best
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utils::str(l3.10 <- nlm3(x0 = c(-10, 10), ndigit = 14, gradtol = 1e-8))
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utils::str(l3.10 <- nlm3(x0 = c(-10, 10), ndigit = 14, gradtol = 1e-8))
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- 
 
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## These tolerances were plucked from thin air: reduced for 32-bit Linux
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chkNlm(l3.10, estimate = c(1,1), # lower tolerances now, notably for fgh:
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chkNlm(l3.10, estimate = c(1,1), # lower tolerances now, notably for fgh:
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       ##                  nl.f   nl.fg  nl.fgh
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       ##                  nl.f   nl.fg  nl.fgh
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       tols = list(min = c(1e-14, 1e-20, 1e-28),
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       tols = list(min = c(1e-9, 1e-20, 1e-16),
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                   est = c(5e-7,  1e-10, 1e-14),
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                   est = c(2e-5,  1e-10, 1e-14),
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                   grad= c(1e-6,  6e-9 , 1e-12)),
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                   grad= c(1e-3,  6e-9 , 1e-12)),
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       codes.wanted = if(Lb64) 1:2 else 1:3)
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       codes.wanted = if(Lb64) 1:2 else 1:3)
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## all 3 fail to converge here
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## all 3 fail to converge here
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utils::str(l3.1c <- nlm3(x0 = c(-100, 100), iterlim = 1000))
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utils::str(l3.1c <- nlm3(x0 = c(-100, 100), iterlim = 1000))
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## i.e., all convergence codes > 1:
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## i.e., all convergence codes > 1: