Nothing
require(optimx)
# test function from book by Owen Jones et al
# function is 2 variables to be maximized
# f(x,y)=sin(x^2/2 - y^2/4)*cos(2x-exp(y))
# ?? seems to have unstable parameter 1
jones<-function(xx){
x<-xx[1]
y<-xx[2]
ff<-sin(x*x/2 - y*y/4)*cos(2*x-exp(y))
ff<- -ff
}
jonesg <- function(xx) {
x<-xx[1]
y<-xx[2]
gx <- cos(x * x/2 - y * y/4) * ((x + x)/2) * cos(2 * x - exp(y)) -
sin(x * x/2 - y * y/4) * (sin(2 * x - exp(y)) * 2)
gy <- sin(x * x/2 - y * y/4) * (sin(2 * x - exp(y)) * exp(y)) - cos(x *
x/2 - y * y/4) * ((y + y)/4) * cos(2 * x - exp(y))
gg <- - c(gx, gy)
}
# Note: No hessian, so newton methods will fail
xx<-0.5*c(pi,pi)
ans<-opm(xx, jones, jonesg, method="ALL", control=list(trace=0))
print(summary(ans, order=value))
# tmp <- readline("continue")
lo<-c(0,0)
up<-c(1.2*pi, 1.2*pi)
ansb<-opm(xx, jones, jonesg, lower=lo, upper=up, method="ALL", control=list(trace=0))
print(summary(ansb, order=value))
# tmp <- readline("continue")
cat("Note: ansbnf shows failure for L-BFGS-B when trying to use forward diff approximation\n")
ansbnf<-opm(xx, jones, gr="grfwd", lower=lo, upper=up, method="ALL", control=list(trace=0))
print(summary(ansbnf, order=value))
# tmp <- readline("continue")
ansbnn<-opm(xx, jones, gr="grnd", lower=lo, upper=up, method="ALL", control=list(trace=0))
print(summary(ansbnn, order=value))
# tmp <- readline("continue")
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