Restart an Optimisation Algorithm
The function provides a simple wrapper for the optimisation algorithms in the package.
restartOpt(fun, n, OF, algo, ...,
method = c("loop", "multicore", "snow"),
mc.control = list(), cl = NULL,
best.only = FALSE)fun |
the optimisation function: |
n |
the number of restarts |
OF |
the objective function |
algo |
the list |
... |
additional data that is passed to the particular optimisation function |
method |
can be |
mc.control |
a list containing settings that will be passed to |
cl |
default is |
best.only |
if |
The argument method determines how fun is
evaluated. Default is loop. If method is "multicore",
function mclapply from package parallel is used. Further
settings for mclapply can be passed through the list
mc.control. If multicore is chosen but the functionality
is not available, then method will be set to loop and a
warning is issued. If method == "snow", function
clusterApply from package parallel is used. In this case,
the argument cl must either be a cluster object (see the
documentation of clusterApply) or an integer. If an integer, a
cluster will be set up via makeCluster(c(rep("localhost", cl)),
type = "SOCK"), and stopCluster is called when the function is
exited. If snow is chosen but parallel is not available
or cl is not specified, then method will be set to
loop and a warning is issued. In case that cl is an
cluster object, stopCluster will not be called automatically.
If best.only is FALSE (the default), the function
returns a list of n lists. Each of the n lists stores
the output of one of the runs.
If best.only is TRUE, only the best restart is
reported. The returned list has the structure specific to the used
method.
Enrico Schumann
Gilli, M., Maringer, D. and Schumann, E. (2019) Numerical Methods and Optimization in Finance. 2nd edition. Elsevier. https://www.elsevier.com/books/numerical-methods-and-optimization-in-finance/gilli/978-0-12-815065-8
Schumann, E. (2019) Financial Optimisation with R (NMOF Manual). http://enricoschumann.net/NMOF.htm#NMOFmanual
## see example(DEopt)
algo <- list(nP = 50L,
F = 0.5,
CR = 0.9,
min = c(-10, -10),
max = c( 10, 10),
printDetail = FALSE,
printBar = FALSE)
## choose a larger 'n' when you can afford it
algo$nG <- 100L
res100 <- restartOpt(DEopt, n = 5L, OF = tfTrefethen, algo = algo)
res100F <- sapply(res100, `[[`, "OFvalue")
algo$nG <- 200L
res200 <- restartOpt(DEopt, n = 5L, OF = tfTrefethen, algo = algo)
res200F <- sapply(res200, `[[`, "OFvalue")
xx <- pretty(c(res100F, res200F, -3.31))
plot(ecdf(res100F), main = "optimum is -3.306",
xlim = c(xx[1L], tail(xx, 1L)))
abline(v = -3.3069, col = "red") ## optimum
lines(ecdf(res200F), col = "blue")
legend(x = "right", box.lty = 0, , lty = 1,
legend = c("optimum", "100 generations", "200 generations"),
pch = c(NA, 19, 19), col = c("red", "black", "blue"))
## a 'best-of-N' strategy: given a sample x of objective
## function values, compute the probability that, after N draws,
## we have at least one realisation not worse than X
x <- c(0.1,.3,.5,.5,.6)
bestofN <- function(x, N) {
nx <- length(x)
function(X)
1 - (sum(x > X)/nx)^N
}
bestof2 <- bestofN(x, 2)
bestof5 <- bestofN(x, 5)
bestof2(0.15)
bestof5(0.15)
## Not run:
## with R >= 2.13.0 and the compiler package
algo$nG <- 100L
system.time(res100 <- restartOpt(DEopt, n = 10L, OF = tfTrefethen, algo = algo))
require("compiler")
enableJIT(3)
system.time(res100 <- restartOpt(DEopt, n = 10L, OF = tfTrefethen, algo = algo))
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.