I have an objective function that could consider a few parameters (a,b,c,d,...) depending on the number of datasets considered. Those parameters are bound in such a way that:
a+b+c+d +..+..<=1
Let suppose I have a,b,c and d; so a+b+c+d<=delta. I define:
a+b < delta-c-d (I call m=delta-c-d)
params = Parameters()
params.add('a', value=0.5, min=0., max=1.)
params.add('m', value=0.5)
params.add('b', expr='m -a')
with m=delta -c-d, so c= delta-m-d (I define n=m-d)
params.add('n', value=0.5)
params.add('delta', value=0.5)
params.add('c', expr='delta-n')
Finally n=m-d ==>d=m-n
params.add('d', expr='m-n')
Is this correct? How to set min and max for m,n and delta?
I followed the hints given on Python lmfit constraints: a < b < c
Cheers, Manuel
I guess that's OK. You can set min/max values on a constraint expression, as with
params.add('c', expr='delta-n', min=0, max=1)
But I should say that (if I understand your question correctly) I would probably do this as
params = Parameters()
params.add('a', value=0.25, min=0, max=1)
params.add('b', value=0.25, min=0, max=1)
params.add('c', value=0.25, min=0, max=1)
params.add('delta', value=0.05, min=0, max=1)
params.add('d', expr='1-(a+b+c+delta)', min=0, max=1)
that seems simpler to me... unless you do want to know the values of your m and n....