Re: Optimisation based on index of data

[Olaf Till]

On Wed, Jan 20, 2016 at 03:18:01AM -0800, inor0627 wrote:
> Hello list,
> 
> I have some 1D measuring data and try to find the subset with best
> linearity. The following approach
> 
> lin_error = @(start_idx) range( detrend(
> data(round(start_idx):round(start_idx)+interesting_length) ) );
> start_opt = round(fminsearch(lin_error,guess));
> 
> works fine for some measurements, but in most cases fminsearch violates the
> bounds of 'data' ('index out of bound') as the optimal subset is near the
> end of 'data'.
> 
> So there are 2 questions:
> Is fminsearch the right choice for this integer-type optimisation? I have a
> bad feeling due to the resulting discontinuities in 'lin_error' when
> rounding 'start_idx'. A google search for 'octave integer optimization'
> didn't answer this question to me.
> If fminsearch is ok for this task, how can I handle the bounds of 'data'?

Hi Ingo,

AFAIK Octave has no function for integer optimization ...

Can you use a brute force approach, checking each index?

If there is indeed some larger distance from your mininimum within
which 'lin_error' does not change monotony, and you're sure your
starting guess is within this distance, you could try to cope with
out-of-bound start indices by letting 'lin_error' return some large
value for them.

No guaranties ...

Olaf

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