Compute f(a) = log(1 +/- exp(-a)) Numerically Optimally
Compute f(a) = log(1 - exp(-a)), respectively g(x) = log(1 + exp(x)) quickly numerically accurately.
log1mexp(a, cutoff = log(2)) log1pexp(x, c0 = -37, c1 = 18, c2 = 33.3)
log1mexp(a) := f(a) = log(1 - exp(-a)) = log1p(- exp(-a)) = log(- expm1(-a))
or, respectively,
log1pexp(x) := g(x) = log(1 + exp(x)) = log1p(exp(x))
computed accurately and quickly.
Martin Maechler, May 2002; log1pexp()
in 2012
Martin Mächler (2012). Accurately Computing \log(1-\exp(-|a|)); https://CRAN.R-project.org/package=Rmpfr/vignettes/log1mexp-note.pdf.
fExpr <- expression( DEF = log(1 - exp(-a)), expm1 = log(-expm1(-a)), log1p = log1p(-exp(-a)), F = log1mexp(a)) a. <- 2^seq(-58, 10, length = 256) a <- a. ; str(fa <- do.call(cbind, as.list(fExpr))) head(fa)# expm1() works here tail(fa)# log1p() works here ## graphically: lwd <- 1.5*(5:2); col <- adjustcolor(1:4, 0.4) op <- par(mfcol=c(1,2), mgp = c(1.25, .6, 0), mar = .1+c(3,2,1,1)) matplot(a, fa, type = "l", log = "x", col=col, lwd=lwd) legend("topleft", fExpr, col=col, lwd=lwd, lty=1:4, bty="n") # expm1() & log1mexp() work here matplot(a, -fa, type = "l", log = "xy", col=col, lwd=lwd) legend("left", paste("-",fExpr), col=col, lwd=lwd, lty=1:4, bty="n") # log1p() & log1mexp() work here par(op) aM <- 2^seqMpfr(-58, 10, length=length(a.)) # => default prec = 128 a <- aM; dim(faM <- do.call(cbind, as.list(fExpr))) # 256 x 4, "same" as 'fa' ## Here, for small 'a' log1p() and even 'DEF' is still good enough l_f <- asNumeric(log(-faM)) all.equal(l_f[,"F"], l_f[,"log1p"], tol=0) # see TRUE (Lnx 64-bit) io <- a. < 80 # for these, the differences are small all.equal(l_f[io,"F"], l_f[io,"expm1"], tol=0) # see 6.662e-9 all.equal(l_f[io,"F"], l_f[io, "DEF" ], tol=0) stopifnot(exprs = { all.equal(l_f[,"F"], l_f[,"log1p"], tol= 1e-15) all.equal(l_f[io,"F"], l_f[io,"expm1"], tol= 1e-7) all.equal(l_f[io,"F"], l_f[io, "DEF" ], tol= 1e-7) }) ## For 128-bit prec, if we go down to 2^-130, "log1p" is no longer ok: aM2 <- 2^seqMpfr(-130, 10, by = 1/2) a <- aM2; fa2 <- do.call(cbind, as.list(fExpr)) head(asNumeric(fa2), 12) tail(asNumeric(fa2), 12) matplot(a, log(-fa2[,1:3]) -log(-fa2[,"F"]), type="l", log="x", lty=1:3, lwd=2*(3:1)-1, col=adjustcolor(2:4, 1/3)) legend("top", colnames(fa2)[1:3], lty=1:3, lwd=2*(3:1)-1, col=adjustcolor(2:4, 1/3)) cols <- adjustcolor(2:4, 1/3); lwd <- 2*(3:1)-1 matplot(a, 1e-40+abs(log(-fa2[,1:3]) -log(-fa2[,"F"])), type="o", log="xy", main = "log1mexp(a) -- approximation rel.errors, mpfr(*, prec=128)", pch=21:23, cex=.6, bg=5:7, lty=1:2, lwd=lwd, col=cols) legend("top", colnames(fa2)[1:3], bty="n", lty=1:2, lwd=lwd, col=cols, pch=21:23, pt.cex=.6, pt.bg=5:7) ## -------------------------- log1pexp() [simpler] -------------------- curve(log1pexp, -10, 10, asp=1) abline(0,1, h=0,v=0, lty=3, col="gray") ## Cutoff c1 for log1pexp() -- not often "needed": curve(log1p(exp(x)) - log1pexp(x), 16, 20, n=2049) ## need for *some* cutoff: x <- seq(700, 720, by=2) cbind(x, log1p(exp(x)), log1pexp(x)) ## Cutoff c2 for log1pexp(): curve((x+exp(-x)) - x, 20, 40, n=1025) curve((x+exp(-x)) - x, 33.1, 33.5, n=1025)
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