问题
I have a function in R that I wish to maximise subject to some simple constraints in optim or constrOptim, but I'm struggling to get my head around ci and uito fit my constraints.
My function is:
negexpKPI <- function(alpha,beta,spend){
-sum(alpha*(1-exp(-spend/beta)))
}
where alpha and beta are fixed vectors, and spend is a vector of spends c(sp1,sp2,...,sp6) which I want to vary in order to maximise the output of negexpKPI. I want to constrain spend in three different ways:
1) Min and max for each sp1,sp2,...,sp6, i.e
0 < sp1 < 10000000
5000 < sp2 < 10000000
...
2) A total sum:
sum(spend)=90000000
3) A sum for some individual components:
sum(sp1,sp2)=5000000
Any help please? Open to any other methods that would work but would prefer base R if possible.
回答1:
According to ?constrOptim:
The feasible region is defined by ‘ui %*% theta - ci >= 0’. The
starting value must be in the interior of the feasible region, but
the minimum may be on the boundary.
So it is just a matter of rewriting your constraints in matrix format. Note, an identity constraint is just two inequality constraints.
Now we can define in R:
## define by column
ui = matrix(c(1,-1,0,0,1,-1,1,-1,
0,0,1,-1,1,-1,1,-1,
0,0,0,0,0,0,1,-1,
0,0,0,0,0,0,1,-1,
0,0,0,0,0,0,1,-1,
0,0,0,0,0,0,1,-1), ncol = 6)
ci = c(0, -1000000, 5000, -1000000, 5000000, 90000000, -90000000)
Additional Note
I think there is something wrong here. sp1 + sp2 = 5000000, but both sp1 and sp2 can not be greater than 1000000. So there is no feasible region! Please fix your question first.
Sorry, I was using sample data that I hadn't fully checked; the true optimisation is for 40
spvalues with 92 constraints which would if I'd replicated here in full would have made the problem more difficult to explain. I've added a few extra zeroes to make it feasible now.
来源:https://stackoverflow.com/questions/40411078/struggling-with-simple-constraints-in-constroptim