• #### I continu about computational complexity by being more and more rigorou

From Wisdom90@21:1/5 to All on Sat Jan 11 15:59:27 2020
Hello,

I continu about computational complexity by being more and more

I said previously(read below) that for example a time complexity such
as n*(log(n)) and n are fuzzy, because we can say that n*(log(n) is
an average resistance(read below to understand the analogy with material resistance) or we can say that n*log(n) is faster than if it was a
quadratic complexity or than an exponential complexity, but we can not
say giving a time complexity of n or n*log(n) how fast it is giving the
input of the n of the time complexity, so since it is not exact
prediction, so it is fuzzy, but this level of fuzziness, like in the
example below of the obese person, permits us to predict important
things in the reality, and this level of fuzziness of computational
complexity is also science, because it is like probability calculations
that permits us to predict, since computational complexity can predict
the resistance of the algorithm if it is high of low or average (by
analogy with material resistance, read below to understand).

Read my previous thoughts to understand:

What is science? and is computational complexity science ?

You just have seen me talking about computational complexity,
but we need to answer the questions of: What is science ?
and is computational complexity science ?

I think that we have to be more smart because there is like
higher level abstractions in science, and we can be in those
abstractions exact precisions of science, but we can be more fuzzy
precisions that are useful and that are also science, to understand me
more, let me give you an example:

If i say that a person is obese, so he has a high risk to get a disease
because he is obese.

Now you are understanding more that with this abstraction we are not
exact precision, but we are more fuzzy , but this fuzziness
is useful and its level of precision is also useful, but is it
science ? i think that this probabilistic calculations are
also science that permits us to predict that the obese person
has a high risk to get a disease. And this probabilistic calculations
are like a higher level abstractions that lack exact precision but
they are still useful precisions. This is how look like computational complexity and its higher level abstractions, so you are immediately understanding that a time complexity of O(n*log(n)) or a O(n)
is like a average level of resistance(read below to know why i am
calling it resistance by analogy with material resistance) when n grows
large, and we can immediately notice that an exponential time complexity
is a low level resistance when n grows large, and we can immediately
notice that a log(n) time complexity is a high level of resistance
when n grows large, so those time complexities are like a higher level abstractions that are fuzzy but there fuzziness, like in the example
above of the obese person, permits us to predict important things in the reality, and this level of fuzziness of computational complexity is also science, because it is like probability calculations that permits us
to predict.

Read the rest of my previous thoughts to understand better:

The why of computational complexity..

of my current answer is below:

=====================================================================
Horand gassmann wrote:

"Where your argument becomes impractical is in the statement "n becomes
large". This is simply not precise enough for practical use. There is a break-even point, call it n_0, but it cannot be computed from the Big-O
alone. And even if you can compute n_0, what if it turns out that the
breakeven point is larger than a googolplex? That would be interesting theoretically, but practically --- not so much."

I don't agree, because take a look below at how i computed the binary
search time complexity, it is a divide and conquer algorithm, and it is
log(n), but we can notice that a log(n) is good when n becomes large, so
this information is practical because a log(n) time complexity is
excellent in practice when n becomes large, and when you look at an
insertion sort you will notice that it is a quadratic time complexity of
n^2, here again, you can feel that it is practical because an quadratic
time complexity is not so good when n becomes large, so you can say that
n^2 is not so good in practice when n becomes large, so
as you are noticing having time complexities of log(n) and n^2
are useful in practice, and for the rest of the the time complexities
you can also benchmark the algorithm in the real world to have an idea
at how it is performing. =================================================================

I think i am understanding better Lemire and Horand gassmann,
they say that if it is not exact needed practical precision, so it is
not science or engineering, but i don't agree with this, because
science and engineering can be like working with more higher level
abstractions that are not exact needed practical precision calculations,
but they can still be useful precision in practice, it is like being a
fuzzy precision that is useful, this is why i think that probabilistic calculations are also scientific , because probabilistic calculations
are useful in practice because they can give us important informations
on the reality that can also be practical, this is why computational
complexity is also useful in practice because it is like a higher level abstractions that are not all the needed practical precision, but it is precision that is still useful in practice, this is why like
probabilistic calculations i think computational complexity is also science.

Read the rest of my previous thoughts to understand better:

More on computational complexity..

Notice how Horand gassmann has answered in sci.math newsgroup:

Horand gassmann wrote the following:

"You are right, of course, on one level. An O(log n)
algorithm is better than an O(n) algorithm *for
large enough inputs*. Lemire understands that, and he
addresses it in his blog. The important consideration
is that _theoretical_ performance is a long way from
_practical_ performance."

And notice how what Lemire wrote about computational complexity:

"But it gets worse: these are not scientific models. A scientific model
would predict the running time of the algorithm given some
implementation, within some error margin. However, these models do
nothing of the sort. They are purely mathematical. They are not
falsifiable. As long as they are mathematically correct, then they are
always true. To be fair, some researchers like Knuth came up with models
that closely mimic reasonable computers, but that’s not what people
pursuing computational complexity bounds do, typically."

So as you are noticing that both of them want to say that computational complexity is far from practical, but i don't agree with them,
because time complexity is like material resistance, and it
informs us on important practical things such as an algorithm of log(n)
time complexity is like much more resistant than O(n) when n becomes
large, and i think this kind of information of time complexity is
practical, this is why i don't agree with Lemire and Horand gassmann,
because as you notice that time complexity is scientific and it is
also engineering. Read the rest of my post to understand more what i
want to say:

I have just read the following webpage of a PhD Computer Scientist and researcher from Montreal, Canada where i am living now from year 1989,
here is the webpage and read it carefully:

Better computational complexity does not imply better speed

https://lemire.me/blog/2019/11/26/better-computational-complexity-does-not-imply-better-speed/

And here is his resume:

https://lemire.me/pdf/resume/resumelemire.pdf

So as you are noticing on the webpage above he is saying the following

"But it gets worse: these are not scientific models. A scientific model
would predict the running time of the algorithm given some
implementation, within some error margin. However, these models do
nothing of the sort. They are purely mathematical. They are not
falsifiable. As long as they are mathematically correct, then they are
always true. To be fair, some researchers like Knuth came up with models
that closely mimic reasonable computers, but that’s not what people
pursuing computational complexity bounds do, typically."

But i don't agree with him because i think he is not understanding
the goal of computational complexity, because when we say that
an algorithm has a time complexity of n*log(n), you have to
understand that it is by logical analogy like saying in physics what is
the material resistance, because the n*log(n) means how well the
algorithm is "amortizing"(it means reducing) the "time" that it takes
taking as a reference of measure the average time complexity of an
algorithm, i said that the time complexity is like the material
resistance in physics, because if the time complexity n grows large, it
is in physics like a big force that you apply to the material that is by logical analogy the algorithm, so if the time complexity is log(n), so
it will amortize the time that it takes very well, so it is in physics
like the good material resistance that amortizes very well the big force
that is applied to it, and we can easily notice that an algorithm
becomes faster, in front of the data that is giving to him, by going
from an exponential time complexity towards a logarithmic time
complexity, so we are noticing that the time complexity is "universal"
and it measures how well the algorithm amortizes(that means it reduces)
the time that it takes taking as a reference of measure the average time complexity of an algorithm, so this is is why computational complexity
is scientific and also it is engineering and it gives us information on
the physical world.

So to give an interesting example of science in computing, we can ask of
what is the time complexity of a binary search algorithm, and here
is my mathematical calculations of its time complexity:

Recurrence relation of a binary search algorithm is: T(n)=T(n/2)+1

Because the "1" is like a comparison that we do in each step of
the divide and conquer method of the binary search algorithm.

So the calculation of the recurrence equation gives:

1st step=> T(n)=T(n/2) + 1

2nd step=> T(n/2)=T(n/4) + 1 ……[ T(n/4)= T(n/2^2) ]

3rd step=> T(n/4)=T(n/8) + 1 ……[ T(n/8)= T(n/2^3) ]

.

.

kth step=> T(n/2^k-1)=T(n/2^k) + 1*(k times)

Adding all the equations we get, T(n) = T(n/2^k) + k times 1

This is the final equation.

So how many times we need to divide by 2 until we have only one element
left?

So it must be:

n/2^k= 1

This gives: n=2^k

this give: log n=k [taken log(base 2) on both sides ]

Put k= log n in the final equation above and it gives:

T(n) = T(1) + log n

T(n) = 1 + log n [we know that T(1) = 1 , because it’s a base condition
as we are left with only one element in the array and that is the
element to be searched so we return 1]

So it gives:

T(n) = O(log n) [taking dominant polynomial, which is n here)

This is how we got “log n” time complexity for binary search.

Thank you,
Amine Moulay Ramdane.

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