But why time methods that the author (me!) has been telling the community for
years have updates? Especially as optimx::optimr() uses same syntax as optim()
and gives access to a number of solvers, both production and didactic. This set
of solvers is being improved or added to regularly, with a major renewal almost
complete (for the adventurous, code on https://github.com/nashjc/optimx).
Note also that the default Nelder-Mead is good for exploring function surface
and
is quite robust at getting quickly into the region of a minimum, but can be
quite
poor in "finishing" the process. Tools have different strengths and weaknesses.
optim() was more or less state of the art a couple of decades ago, but there are
other choices now.
JN
On 2023-08-08 05:14, Sami Tuomivaara wrote:
Thank you all very much for the suggestions, after testing, each of them would
be a viable solution in certain contexts. Code for benchmarking:
# preliminaries
install.packages("microbenchmark")
library(microbenchmark)
data <- new.env()
data$ans2 <- 0
data$ans3 <- 0
data$i <- 0
data$fun.value <- numeric(1000)
# define functions
rosenbrock_env <- function(x, data)
{
x1 <- x[1]
x2 <- x[2]
ans <- 100 * (x2 - x1 * x1)^2 + (1 - x1)^2
ans2 <- ans^2
ans3 <- sqrt(abs(ans))
data$i <- data$i + 1
data$fun.value[data$i] <- ans
ans
}
rosenbrock_env2 <- function(x, data)
{
x1 <- x[1]
x2 <- x[2]
ans <- 100 * (x2 - x1 * x1)^2 + (1 - x1)^2
ans2 <- ans^2
ans3 <- sqrt(abs(ans))
data$ans2 <- ans2
data$ans3 <- ans3
ans
}
rosenbrock_attr <- function(x)
{
x1 <- x[1]
x2 <- x[2]
ans <- 100 * (x2 - x1 * x1)^2 + (1 - x1)^2
ans2 <- ans^2
ans3 <- sqrt(abs(ans))
attr(ans, "ans2") <- ans2
attr(ans, "ans3") <- ans3
ans
}
rosenbrock_extra <- function(x, extraInfo = FALSE)
{
x1 <- x[1]
x2 <- x[2]
ans <- 100 * (x2 - x1 * x1)^2 + (1 - x1)^2
ans2 <- ans^2
ans3 <- sqrt(abs(ans))
if (extraInfo) list(ans = ans, ans2 = ans2, ans3 = ans3)
else ans
}
rosenbrock_all <- function(x)
{
x1 <- x[1]
x2 <- x[2]
ans <- 100 * (x2 - x1 * x1)^2 + (1 - x1)^2
ans2 <- ans^2
ans3 <- sqrt(abs(ans))
list(ans = ans, ans2 = ans2, ans3 = ans3)
}
returnFirst <- function(fun) function(...) do.call(fun,list(...))[[1]]
rosenbrock_all2 <- returnFirst(rosenbrock_all)
# benchmark all functions
set.seed <- 100
microbenchmark(env = optim(c(-1,2), rosenbrock_env, data = data),
env2 = optim(c(-1,2), rosenbrock_env2, data = data),
attr = optim(c(-1,2), rosenbrock_attr),
extra = optim(c(-1,2), rosenbrock_extra, extraInfo = FALSE),
all2 = optim(c(-1,2), rosenbrock_all2),
times = 100)
# correct parameters and return values?
env <- optim(c(-1,2), rosenbrock_env, data = data)
env2 <- optim(c(-1,2), rosenbrock_env2, data = data)
attr <- optim(c(-1,2), rosenbrock_attr)
extra <- optim(c(-1,2), rosenbrock_extra, extraInfo = FALSE)
all2 <- optim(c(-1,2), rosenbrock_all2)
# correct return values with optimized parameters?
env. <- rosenbrock_env(env$par, data)
env2. <- rosenbrock_env(env2$par, data)
attr. <- rosenbrock_attr(attr$par)
extra. <- rosenbrock_extra(extra$par, extraInfo = FALSE)
all2. <- rosenbrock_all2(all2$par)
# functions that return more than one value
all. <- rosenbrock_all(all2$par)
extra2. <- rosenbrock_extra(extra$par, extraInfo = TRUE)
# environment values correct?
data$ans2
data$ans3
data$i
data$fun.value
microbenchmarking results:
Unit: microseconds
expr min lq mean median uq max neval
env 644.102 3919.6010 9598.3971 7950.0005 15582.8515 42210.900 100
env2 337.001 351.5510 479.2900 391.7505 460.3520 6900.800 100
attr 350.201 367.3010 502.0319 409.7510 483.6505 6772.800 100
extra 276.800 287.2010 402.4231 302.6510 371.5015 6457.201 100
all2 630.801 646.9015 785.9880 678.0010 808.9510 6411.102 100
rosenbrock_env and _env2 functions differ in that _env accesses vectors in the
defined environment by indexing, whereas _env2 doesn't (hope I interpreted this
right?). This appears to be expensive operation, but allows saving values
during the steps of the optim iteration, rather than just at convergence.
Overall, _extra has consistently lowest median execution time!
My earlier workaround was to write two separate functions, one of which returns
extra values; all suggested approaches simplify that approach considerably. I
am also now more educated about attributes and environments that I did not know
how to utilize before and that proved to be very useful concepts. Again, thank
you everyone for your input!
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