[SciPy-user] optimization advice needed
Sun Jan 27 15:52:39 CST 2008
If f() is stationary and you are trying to estimate a and b, isn't this
exactly the case of a Kalman filter for linear f()? And if f() is
non-linear, there are extensions to the Kalman framework to handle this.
Neal Becker wrote:
> I have an optimization problem that doesn't quite fit in the usual
> The problem is to minimize the mean-square-error between a sequence of noisy
> observations and a model.
> Let's suppose there are 2 parameters in the model: (a,b)
> So we observe g = f(a,b) + n.
> Assume all I know about the problem is it is probably convex.
> Now a couple of things are unusual:
> 1) The problem is not to optimize the estimates (a',b') one time - it is
> more of an optimal control problem. (a,b) are slowly varying, and we want
> to continuously refine the estimates.
> 2) We want an inversion of the usual control. Rather than having the
> optimization algorithm call my function, I need my function to call the
> optimization. Specifically I will generate one _new_ random vector of
> observations. Then I want to perform one iteration of the optimization on
> this observation. (In the past, I have adapted the simplex algorithm to
> work this way).
> So, any advice on how to proceed?
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