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gradient.search(sample, error, minerr = 1e-05,
start = mean(sample), theta = NULL)
# minimize L1 norm:
L1.error <- function(sample, Shat) {
sum(dist(sample, Shat, method="intrinsic", p=1))
}
cayley.sample <- ruars(n = 10, rangle = rcayley, nu = 1, space = 'SO3')
SL1 <- gradient.search(cayley.sample, L1.error, start=id.SO3)
# visually no perceptible difference between median estimates from in-built function and
# gradient based search (for almost all starting values)
plot(cayley.sample, center=SL1$Shat, show_estimates="all")
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