Grid Search
Evaluate a function for a given list of possible arguments.
gridSearch(fun, levels, ..., lower, upper, npar = 1L, n = 5L,
printDetail = TRUE,
method = NULL,
mc.control = list(), cl = NULL,
keepNames = FALSE, asList = FALSE)fun |
a function of the form |
levels |
a list of levels for the arguments (see Examples) |
... |
objects passed to |
lower |
a numeric vector. Ignored if levels are explicitly specified. |
upper |
a numeric vector. Ignored if levels are explicitly specified. |
npar |
the number of parameters. Must be supplied if |
n |
the number of levels. Default is 5. Ignored if levels are explicitly specified. |
printDetail |
print information on the number of objective function evaluations |
method |
can be |
mc.control |
a list containing settings that will be passed to |
cl |
default is |
keepNames |
|
asList |
does |
A grid search can be used to find ‘good’ parameter values for a
function. In principle, a grid search has an obvious deficiency: as
the length of x (the first argument to fun) increases,
the number of necessary function evaluations grows exponentially. Note
that gridSearch will not warn about an unreasonable number of
function evaluations, but if printDetail is TRUE it will
print the required number of function evaluations.
In practice, grid search is often better than its reputation. If a function takes only a few parameters, it is often a reasonable approach to find ‘good’ parameter values.
The function uses the mechanism of expand.grid to create
the list of parameter combinations for which fun is evaluated; it
calls lapply to evaluate fun if
method == "loop" (the default).
If method is multicore, then 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", the 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 not available or cl is not specified,
then method will be set to loop and a warning is issued.
A list.
minfun |
the minimum of |
minlevels |
the levels that give this minimum. |
values |
a list. All the function values of |
levels |
a list. All the levels for which |
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
testFun <- function(x)
x[1L] + x[2L]^2
sol <- gridSearch(fun = testFun, levels = list(1:2, c(2, 3, 5)))
sol$minfun
sol$minlevels
## specify all levels
levels <- list(a = 1:2, b = 1:3)
res <- gridSearch(testFun, levels)
res$minfun
sol$minlevels
## specify lower, upper and npar
lower <- 1; upper <- 3; npar <- 2
res <- gridSearch(testFun, lower = lower, upper = upper, npar = npar)
res$minfun
sol$minlevels
## specify lower, upper, npar and n
lower <- 1; upper <- 3; npar <- 2; n <- 4
res <- gridSearch(testFun, lower = lower, upper = upper, npar = npar, n = n)
res$minfun
sol$minlevels
## specify lower, upper and n
lower <- c(1,1); upper <- c(3,3); n <- 4
res <- gridSearch(testFun, lower = lower, upper = upper, n = n)
res$minfun
sol$minlevels
## specify lower, upper (auto-expanded) and n
lower <- c(1,1); upper <- 3; n <- 4
res <- gridSearch(testFun, lower = lower, upper = upper, n = n)
res$minfun
sol$minlevelsPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.