But r-approximate is still worst-case complexity, right?

To the extent the human mind can solve NP-complete problems, it does so in
a roughly "probably approximately correct"  manner -- i.e. most of the time
it can get a pretty good solution....    This is quite different than
getting within some factor of r of the correct solution every time...

-- Ben



On Tue, Feb 25, 2014 at 3:46 PM, YKY (Yan King Yin, 甄景贤) <
[email protected]> wrote:

> On Tue, Feb 25, 2014 at 3:19 PM, Ben Goertzel <[email protected]> wrote:
>
>>
>> Hmmm...
>>
>>
>>> .  But if approximate solutions exist, then it seems natural that we can
>>> throw more resources at it to get it closer and closer to exact solutions.
>>>  It is unlikely that exact solutions would abruptly become impossible.
>>>
>>
>>
>> Hmmm.  Among other issues, that really depends on how much more resources
>> you need to throw at a problem, to get each additional epsilon of
>> accuracy... no?
>>
>
>
> There're these "hardness of approximability" results that say, for
> example, to design an r-approximate algorithm with small r would still
> imply that P = NP.
>
> By r-approximate it means the algorithm can find a solution whose cost is
> r times the optimum. [ Trevisan 2004: "Inapproximability of combinatorial
> optimization problems" ]
>
> Though I suspect there're other [better?] criteria of approximation that
> can be defined...  but the result may be the same, ie, approximate
> polynomial-time solution would imply exact polynomial-time solution.
>
> =)
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-- 
Ben Goertzel, PhD
http://goertzel.org

"In an insane world, the sane man must appear to be insane". -- Capt. James
T. Kirk



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