Right aren't monotonic signals the worst case here? Or maybe not, since
they're worst for one wedge, but best for the other?

Ethan D

On Fri, Sep 2, 2016 at 10:12 AM, Evan Balster <e...@imitone.com> wrote:

> Just a few clarifications:
>
> - Local maxima and first difference don't really matter.  The maximum
> wedge describes global maxima for all intervals [t-x, t], where x=[R-1..0].
>
> - If two equal samples are encountered, the algorithm can forget about the
> older of them.
>
> It has been very helpful for my purposes to visualize the algorithm thus:
>  Imagine drawing lines rightward from all points on the signal.  Those
> which extend to the present time without intersecting other parts of the
> signal form the minimum and maximum wedge.  The newest sample, and only the
> newest sample, exists in both wedges.
>
> [image: Inline image 3]
> http://interactopia.com/archive/images/lemire_algorithm.png
>
> The algorithm can safely forget anything in grey because it has been
> "shadowed" by newer maximum or minimum values.
>
> – Evan Balster
> creator of imitone <http://imitone.com>
>
> On Fri, Sep 2, 2016 at 1:50 AM, Ross Bencina <rossb-li...@audiomulch.com>
> wrote:
>
>> On 2/09/2016 4:37 PM, Ross Bencina wrote:
>>
>>> When the first difference is positive, the history is trimmed. This is
>>> the only time any kind of O(N) or O(log2(N)) operation is performed.
>>> First difference positive implies that a new local maximum is achieved:
>>> in this case, all of the most recent history samples that are less than
>>> the new maximum are trimmed.
>>>
>>
>> Correction:
>>
>> [The new local maximum dominates all
>>> history up to the most recent *history sample* that exceeds it, which is
>>> retained.]
>>>
>>
>> I had said "most recent local maximum" but the history contains a
>> monotonic non-increasing sequence.
>>
>> Ross.
>>
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