[英]minimize a multivariate function in R with constraints
我正在嘗試最小化 R 中的函數S.residuum
,但有一些限制
S.residuum<- function(w, stdv, corr) {
intermed<-0
for (i in 1:length(stdvv)) {
intermed =intermed+residuum(w,stdvv,corr.mat,i)
}
return(intermed)
}
其中w
是長度為 6 的向量。 約束如下所示:
0.03 <= w1 <= 0.27
0.03 <= w2 <= 0.27
0.20 <= w3 <= 0.91
0.01 <= w4 <= 0.1
0.01 <= w5 <= 0.1
0.01 <= w6 <= 0.1
到目前為止,我能夠實現它:
nlminb(c(1,1,1,1,1,1),S.residuum,hessian = NULL,
lower=c(0.03,0.03,0.2,0.01,0.01), upper=c(0.27,0.27,0.91,0.1,0.1)),
其中c(1,1,1,1,1,1)
是初始值。
但是,我還有 2 個其他限制。 我把第一個寫成函數:
nequal <- function(w,stdv, corr) {
intermed<-0
for (j in 1:length(stdvv)) {
for (i in 1:length(stdvv)) {
intermed =intermed+ w[[i]] * w[[j]] * stdv[[i]] * stdv[[j]] * corr[[i]][[j]]
}
}
intermed=sqrt(intermed)
},
其中stdv
是向量, corr
是矩陣。 應滿足以下約束條件:
1) nequal <=0.75
2) w1+w2+w3+w4+w5+w6=1
有人可以對我說我怎么能在 R 中做到這一點? 謝謝!
您可以使用Rsolnp包中的函數solnp 。 代碼如下所示:
library(Rsolnp)
# Inequality constraint
nequal <- function(w) {
intermed <- 0
for (j in 1:length(stdvv)) {
for (i in 1:length(stdvv)) {
intermed = intermed + w[[i]] * w[[j]] * stdvv[[i]] * stdvv[[j]] * corr.mat[[i]][[j]]
}
}
sqrt(intermed)
}
# Equality constraint
equal <- function(w) {
w[[1]]+w[[2]]+w[[3]]+w[[4]]+w[[5]]+w[[6]]
}
# Minimization with constraints
min <- solnp(c(0., 0., 0., 0., 0., 0.),
S.residuum,
eqfun = equal,
eqB = 1,
ineqfun = nequal,
ineqLB = 0,
ineqUB = 0.075,
LB = c(0.03, 0.03, 0.2, 0.01, 0.01, 0.01),
UB = c(0.27, 0.27, 0.91, 0.1, 0.1, 0.1))
我在這里找到了constrOptim()
,它對我來說效果很好。
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