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thanks, Scott

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If the objective function has the form

F = f

the answer is yes.

F = f

^{2}+ g^{2}+ h^{2}+ ...the answer is yes.

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I'd like to find the smoothing parameters which minimize the sum of the squared errors of a triple exponential smoothing fit. So, F is just the sum of the squared difference between the true values and the fitted values. But, the fitted values are computed iteratively over the time series from updating values of the level, trend and season components.

How would I formulate this problem to use the solver?

thanks, Scott

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N = x

(where there are N observations x, and their mean appears in the smoothing expression) cannot be handled directly. You can try to recast your problem in this form; if you post details of your smoothing function, I'll try to help.

Should it prove impossible to recast the problem as a bound-constrained least-squares problem, you will need to use another solver than the one in MKL. See the most useful Decision Tree for Optimization Software by Professor H.J. Mittelmann.

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Since the nonlinear least square problems is:

min(sum(Fi(x)^2)) where i=1..m and x=(x1,..,xn) and m >= n

where the constraints are on Fi(x)

My problem could be:

min(sum(Ft(alpha,beta,gamma)^2)) where t=1..T and x=(alpha,beta,gamma) and T >= 3

where Ft returns the residual for period t

But, my constraints are: 0 <= alpha, beta, gamma <= 1 when I need constaints on F instead.

And, I don't see any way to reformulate the constraints in terms of F.

Plus, the number of periods T=m could be large and I don't think this solver is intended to handle large m.

Thanks for the pointer to Decision Tree site.

I'll find something outside of MKL.

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*> when I need constaints on F instead.*

Why do you say that? Your constraints are bounds (-,1) on and (0, +) on and . These should work fine, with something such as 1D20 used in place of .

The MKL routine should handle expressions with a few hundred terms without running into problems.

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There is no way to derive constraints on F from the contraints on X because F isn't that simple.

Anyway, I've implemented constrained multivariate steepest descent which seems to work fine for now.

What I really want is for MKL to implement some general optimization.

In particular, I would like: L-BFGS-C.

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*> Nope, the constraints have to be on F because that's the way the method works and my constraints are on X.*

I cannot agree or disagree with that statement because it makes no sense to me. Which "method" is it that you mean here?

Before you made the claim in #4, there was nothing stated to indicate that you had constraints on F

_{i}. In the later posts, I see a statement that such constraints exist, but no supporting evidence or examples of such constraints.

For each equality constraint on a

*function*of a single F

_{i}, you can find one or more equality constraint on F

_{i}, and that F

_{i}can be taken out of the objective expression.

L-BFGS is an algorithm for

*unconstrained*optimization.

Steepest descent is a very inefficient algorithm for unconstrained minimization.

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