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require(optimx)
# Note that solving 1 dimensional problems with multi-dimensional
# minimizers can be unpredictable!
f1<-function(xx){ # function of one parameter
((3*xx+2)*xx-5)*xx+4 ## 3*xx^3 + 2*xx^2 -5*xx + 4
}
g1 <- function(xx){
gg <- 9*xx*xx+4*xx -5
}
x0 <- .1234
lb <- -1
ub <- 10
cat("R has optimize function for [1D] functions\n")
aoptimize <- optimize(f1, c(-1, 10))
print(aoptimize)
cat("or we can use optim() with method='Brent' \n")
abrent <- optim(x0, f1, lower=lb, upper=ub, method="Brent", control=list(trace=0))
print(abrent)
ansone<-opm(x0, f1, g1, method = c("Rvmmin", "Rcgmin"), control=list(trace=0))
ansone.sum<-summary(ansone, order=value)
print(ansone.sum)
# test 2
x0 = 1
cat("Start with x0=1. Use optim() with method='Brent' \n")
abrent1 <- optim(x0, f1, lower=lb, upper=ub, method="Brent", control=list(trace=0))
print(abrent1)
ansone1<-opm(x0, f1, g1, lower=lb, upper=ub, method = c("Rvmmin", "Rcgmin"), control=list(trace=0))
ansone1.sum<-summary(ansone1, order=value)
print(ansone1.sum)
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