------------------- Version 2 of my problem improving the definition of what
the optimal solution would be.
Dear all,
I'm working on the following problem:
Assume two datasets: Y, Y that represent the same physical quantity Q. Dataset
X contains values of Q after an event A while dataset Y contains values of Q
after an event B.
In R X, Y are vectors of the same length, containing effectivelly a number of
observations of Q in each state.
Q is a continous variable.
Now, the two datasets should ideally not have any range of overlapping values.
That is
max(x) << min (Y)
but that is not the reality of the problem. there are usually overlaps, bigger
or smaller.
Now, what I want to do is the following:
Suppose that we choose a value P so that.
Any X <= P is understood as belonging to group X while
any Y > P is understood as belonging to group Y.
now any values of X > P or of Y <= P are wrongly understood as belonging to Y
nad X effectively.
Hence we have Xerr -- > Sum( X >P) and Yerror --> Sum(Y<=P).
I want to solve this bivariate optimization problem where I want to at the same
time minimize the error of X and Y for a given P. Ultimately the target is to
optimize the value of P so that the errors of both X and Y are optimized. More
specifically, the optimal solution is one where
1. The total error (Xerr + Yerr) is minimized
2. The values or Xerr and Yerr are balanced as much as possible, probably.
Does any1 have some functions in mind that can help with parts of this problem
? It's not impossible to write the algorithm but it will take time and things
like convergence and robustness need to be checked.... !
thank you for your help.
Best regards,
Marios Barlas
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