Hi Rui,
I was not aware of this function, however it looks like it should work for
me. Many thanks for this intuition.
However, in my optimization there are 3 constraints as below,
1) sum of all parameters should be 1
2) all parameters should be positive
3) last parameter should be greater than
Às 13:59 de 28/03/2025, Daniel Lobo escreveu:
Hi Duncan,
Thanks for your comment, I agree with that.
But, how it can be justified that an Optimizer gives a result which is
inferior to the starting value? At most, resulting value can remain at the
same level, isnt it?
On Fri, 28 Mar 2025 at 14:
That's a question for the maintainer of the package you used.
Duncan Murdoch
On 2025-03-28 9:59 a.m., Daniel Lobo wrote:
Hi Duncan,
Thanks for your comment, I agree with that.
But, how it can be justified that an Optimizer gives a result which is
inferior to the starting value? At most, resu
Every time I give a seminar on optimization (most recently in Feb
at Univ Cote d'Azur -- thank you Yassine for the welcome!) I point out
Algorithms CONVERGE
Programs TERMINATE
If you race a Maserati (fmincon?) on a dirt bike course, you'll likely
get stuck on the first mud mound, which co
Hi Duncan,
Thanks for your comment, I agree with that.
But, how it can be justified that an Optimizer gives a result which is
inferior to the starting value? At most, resulting value can remain at the
same level, isnt it?
On Fri, 28 Mar 2025 at 14:34, Duncan Murdoch
wrote:
> I haven't run your
I haven't run your code, but since Kendall correlation is based on
ranks, your Fn is probably locally constant with jumps when the ranks
change. That's a really hard kind of function to maximize, and the
algorithm used by fmincon is not appropriate to do it.
Sorry, but I don't know if there i
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