Hello to everyone. I am trying to replicate a simple optimization that I did in microsof excel in R. I have some timeseries of certain models and I want to find the multipliers (i,j,k,l) that minimise the sum of squared residuals of the average model to the observed values (temps$baltic_45_65_may). For some reason, the outcome violates the constraints I have put. I am completely new to this and not very proficient in R so excuse me if I didn’t formulate the question correctly.

res <- function(i,j,k,l){

res = sum(((i*temps$nemo_45_65_may_box2+
j*temps$mom_45_65_may_box2+

k

*temps$getm_clml11_45_65_may_box2+*

ltemps$getm_uerral03_45_65_may_box2)-temps$baltic_45_65_may)^2)

l

}

library(CVXR)

# the variable

i <- Variable(1)

j <- Variable(1)

k <- Variable(1)

l <- Variable(1)

# objective

objective <- Minimize(res(i,j,k,l))

# define problem

constraint1 <- i+j+k+l == 1

constraint2 <- i >= 0

constraint3 <- j >= 0

constraint4 <- k >= 0

constraint5 <- l >= 0

problem <- Problem(objective, constraints = list(constraint1, constraint2, constraint3, constraint4, constraint5))

# solve problem

result <- solve(problem)

# results

result$getValue(i) # optimal x

result$getValue(j)

result$getValue(k)

result$getValue(l)