If the data is fairly small, send it and the objective function to me off-list and I'll
give it a quick try.
However, this looks very much like the kind of distance-constrained type of problem like
the "largest small polygon" i.e., what is the maximum area hexagon where no vertex is more
than 1 unit from another. (It is NOT a regular hexagon! More like a dented pentagon.)
Such problems are often better posed using polar coordinates, but the setup
takes work.
If you are going to have to do a lot of these problems, it will be worthwhile looking into
ways to get very good starts, in which case a very crude method using penalty or barrier
functions could be effective.
John Nash
From: Brandon Zicha <brandon.zi...@ua.ac.be>
Subject: [R] non-linear contrained optimization
Message-ID: <33c13a02-f603-410a-871d-e78dde272...@ua.ac.be>
Content-Type: text/plain; charset=US-ASCII; format=flowed; delsp=yes
I have searched the previous help boards and discovered the problem
with Rdonlp2 - Specifically, its non-availability. I thought that this
was my solution, but perhaps there is a better way that you all could
help me with. I imagine that this problem is trivial to people such
as the experts on this mailing list.
I am trying to solve this problem over and over again in a simulation:
I want to find the values of x and y which minimize
f(x,y) = sqrt((z-x)2+(w-y)2
subject to the constraints:
0=< sqrt((z2-x)2+(w2-y)2) - d2
0=< sqrt((z3-x)2+(w3-y)2) - d3
.....
0=< sqrt((zk-x)2+(wk-y)2) - dk
where zi, wi, di are known scalars.
I would appreciate any help with how to implement this in R.
Many thanks,
Brandon Z.
University of Antwerp
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