# NOT RUN {
#####################
## All examples for the Rvmmin package are in this .Rd file
##
## Rosenbrock Banana function
fr <- function(x) {
x1 <- x[1]
x2 <- x[2]
100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}
ansrosenbrock <- Rvmmin(fn=fr,gr="grfwd", par=c(1,2))
print(ansrosenbrock)
cat("\n")
cat("No gr specified as a test\n")
ansrosenbrock0 <- Rvmmin(fn=fr, par=c(1,2))
print(ansrosenbrock0)
# use print to allow copy to separate file that can be called using source()
#####################
# Simple bounds and masks test
#
# The function is a sum of squares, but we impose the
# constraints so that there are lower and upper bounds
# away from zero, and parameter 6 is fixed at the initial
# value
bt.f<-function(x){
sum(x*x)
}
bt.g<-function(x){
gg<-2.0*x
}
n<-10
xx<-rep(0,n)
lower<-rep(0,n)
upper<-lower # to get arrays set
bdmsk<-rep(1,n)
bdmsk[(trunc(n/2)+1)]<-0
for (i in 1:n) {
lower[i]<-1.0*(i-1)*(n-1)/n
upper[i]<-1.0*i*(n+1)/n
}
xx<-0.5*(lower+upper)
cat("Initial parameters:")
print(xx)
cat("Lower bounds:")
print(lower)
cat("upper bounds:")
print(upper)
cat("Masked (fixed) parameters:")
print(which(bdmsk == 0))
ansbt<-Rvmmin(xx, bt.f, bt.g, lower, upper, bdmsk, control=list(trace=1))
print(ansbt)
#####################
# A version of a generalized Rosenbrock problem
genrose.f<- function(x, gs=NULL){ # objective function
## One generalization of the Rosenbrock banana valley function (n parameters)
n <- length(x)
if(is.null(gs)) { gs=100.0 }
fval<-1.0 + sum (gs*(x[1:(n-1)]^2 - x[2:n])^2 + (x[2:n] - 1)^2)
return(fval)
}
genrose.g <- function(x, gs=NULL){
# vectorized gradient for genrose.f
# Ravi Varadhan 2009-04-03
n <- length(x)
if(is.null(gs)) { gs=100.0 }
gg <- as.vector(rep(0, n))
tn <- 2:n
tn1 <- tn - 1
z1 <- x[tn] - x[tn1]^2
z2 <- 1 - x[tn]
gg[tn] <- 2 * (gs * z1 - z2)
gg[tn1] <- gg[tn1] - 4 * gs * x[tn1] * z1
gg
}
# analytic gradient test
xx<-rep(pi,10)
lower<-NULL
upper<-NULL
bdmsk<-NULL
genrosea<-Rvmmin(xx,genrose.f, genrose.g, gs=10)
genrosenf<-Rvmmin(xx,genrose.f, gr="grfwd", gs=10) # use local numerical gradient
genrosenullgr<-Rvmmin(xx,genrose.f, gs=10) # no gradient specified
cat("genrosea uses analytic gradient\n")
print(genrosea)
cat("genrosenf uses grfwd standard numerical gradient\n")
print(genrosenf)
cat("genrosenullgr has no gradient specified\n")
print(genrosenullgr)
cat("Other numerical gradients can be used.\n")
cat("timings B vs U\n")
lo<-rep(-100,10)
up<-rep(100,10)
bdmsk<-rep(1,10)
tb<-system.time(ab<-Rvmminb(xx,genrose.f, genrose.g, lower=lo, upper=up, bdmsk=bdmsk))[1]
tu<-system.time(au<-Rvmminu(xx,genrose.f, genrose.g))[1]
cat("times U=",tu," B=",tb,"\n")
cat("solution Rvmminu\n")
print(au)
cat("solution Rvmminb\n")
print(ab)
cat("diff fu-fb=",au$value-ab$value,"\n")
cat("max abs parameter diff = ", max(abs(au$par-ab$par)),"\n")
# Test that Rvmmin will maximize as well as minimize
maxfn<-function(x) {
n<-length(x)
ss<-seq(1,n)
f<-10-(crossprod(x-ss))^2
f<-as.numeric(f)
return(f)
}
negmaxfn<-function(x) {
f<-(-1)*maxfn(x)
return(f)
}
cat("test that maximize=TRUE works correctly\n")
n<-6
xx<-rep(1,n)
ansmax<-Rvmmin(xx,maxfn, gr="grfwd", control=list(maximize=TRUE,trace=1))
print(ansmax)
cat("using the negmax function should give same parameters\n")
ansnegmax<-Rvmmin(xx,negmaxfn, gr="grfwd", control=list(trace=1))
print(ansnegmax)
#####################
cat("test bounds and masks\n")
nn<-4
startx<-rep(pi,nn)
lo<-rep(2,nn)
up<-rep(10,nn)
grbds1<-Rvmmin(startx,genrose.f, genrose.g, lower=lo,upper=up)
print(grbds1)
cat("test lower bound only\n")
nn<-4
startx<-rep(pi,nn)
lo<-rep(2,nn)
grbds2<-Rvmmin(startx,genrose.f, genrose.g, lower=lo)
print(grbds2)
cat("test lower bound single value only\n")
nn<-4
startx<-rep(pi,nn)
lo<-2
up<-rep(10,nn)
grbds3<-Rvmmin(startx,genrose.f, genrose.g, lower=lo)
print(grbds3)
cat("test upper bound only\n")
nn<-4
startx<-rep(pi,nn)
lo<-rep(2,nn)
up<-rep(10,nn)
grbds4<-Rvmmin(startx,genrose.f, genrose.g, upper=up)
print(grbds4)
cat("test upper bound single value only\n")
nn<-4
startx<-rep(pi,nn)
grbds5<-Rvmmin(startx,genrose.f, genrose.g, upper=10)
print(grbds5)
cat("test masks only\n")
nn<-6
bd<-c(1,1,0,0,1,1)
startx<-rep(pi,nn)
grbds6<-Rvmmin(startx,genrose.f, genrose.g, bdmsk=bd)
print(grbds6)
cat("test upper bound on first two elements only\n")
nn<-4
startx<-rep(pi,nn)
upper<-c(10,8, Inf, Inf)
grbds7<-Rvmmin(startx,genrose.f, genrose.g, upper=upper)
print(grbds7)
cat("test lower bound on first two elements only\n")
nn<-4
startx<-rep(0,nn)
lower<-c(0,1.1, -Inf, -Inf)
grbds8<-Rvmmin(startx,genrose.f,genrose.g,lower=lower, control=list(maxit=2000))
print(grbds8)
cat("test n=1 problem using simple squares of parameter\n")
sqtst<-function(xx) {
res<-sum((xx-2)*(xx-2))
}
nn<-1
startx<-rep(0,nn)
onepar<-Rvmmin(startx,sqtst, gr="grfwd", control=list(trace=1))
print(onepar)
cat("Suppress warnings\n")
oneparnw<-Rvmmin(startx,sqtst, gr="grfwd", control=list(dowarn=FALSE,trace=1))
print(oneparnw)
# }
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