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Subject: Re: Quadratic Cost Function x^T Q x
Date: Thu, 20 May 2010 21:47:14 +0000 (UTC)
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"Jason" <jf203@ic.ac.uk> wrote in message <ht495s$eq2$1@fred.mathworks.com>...
> "Roger Stafford" <ellieandrogerxyzzy@mindspring.com.invalid> wrote in message 

> Think of the vector x as a line in geometrical 2-D space. I want to estimate the parameters of this line, i.e. x(1)*x + x(2)*y + x(3).
>
> Pardon my ignorance, but even without placing any constraints on x you get a non-trivial solution (i.e. not x = 0).
===============

But the cost function for this estimation problem bears no resemblance to the one you posed.


> 
> My main question is if I can use something different than lsqnonlin which takes a very long time to run and is prone to fall into suboptimal minima.
=================

You might try running lsqnonlin with the Algorithm option to 'levenberg-marquardt'. My intuition about the default trust-region method is that, even if you initialize in the correct capture basin, the algorithm can crawl out of it into something less optimal. Conversely, Levenberg-Marquardt is, according to my intuition, may be more basin-preserving. Also, if you supply an analytical gradient then, according to doc lsqnonlin, it could be more computationally cheap per iteration. 

All of this assumes that you have at least a reasonable guess of the initial solution, but there's no escaping that when it comes to non-convex minimization.