From: d@d.d   
      
   Hello,   
      
   Read this:   
      
      
   Read again , i correct because i think fast and i write fast..   
      
   About the classification of complexities in computational complexity..   
      
   I think there is something happening in computational complexity,   
   le's take for example the time complexity, since formally when we have a   
   compositional of complexities like having an f(n) and g(n) time   
   complexities, computational complexity says that  there compositional   
   complexity by adding them is max(f(n),g(n)), but this is too "fuzzy" and   
   it lacks precision for better classification of time complexities, since   
   time complexities are like material resistance in physics, this is why i   
   am for example saying that a time complexity of n*log(n) and n are   
   average resistance compared with other complexities that exists(read my   
   below analogy with material resistance), this way i am classifying, so   
   i think the right way is that in compositional complexity by adding of   
   two time complexities of for example f(n) and g(n) is to take the average   
   of (f(n)+g(n))/2 and we can formally generalize the calculation, this   
   way we are not going to loose precision to be better classification   
   of complexities, like in classification of material resistance in physics.   
      
   Yet more rigorous about computational complexity..   
      
      
   I said the following (read below)   
      
   "I said previously(read below) that for example the time complexities   
   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 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 or low or average (by   
   analogy with material resistance, read below to understand)."   
      
      
   Hope that you are understanding my abstract reasoning that in   
   for example time complexity, the resistance of the algorithm is like the   
   material resistance in physics , and it is the resistance of the   
   algorithm compared to other complexities, it is not resistance of the   
   algorithm in front of the input that is giving. And this prediction   
   in computational complexity is very important because it predicts   
   that the algorithm is the one that is this resistance that exists in   
   reality, it is like material resistance in physics. (read below my   
   analogy with material resistance).   
      
      
   More rigorous about precision of computational complexity..   
      
   When we say that 2+2=4, it is a system that inherently contains enough   
   precision that we call enough precision, it is in fuzzy logic that it is   
   100% precision that is a truth that is equal 1   
      
   But when for example we say:   
      
   "If i say that a person is obese, so he has a high risk to get a disease   
   because he is obese."   
      
   What is it equal in fuzzy logic ?   
      
   It is not an exact value in fuzzy logic, but it is not exact precision   
   but it is fuzzy and it is equal to a high risk to get a disease.   
      
   That's the same for time complexities of n*log(n) and n , they   
   are fuzzy, but there level of fuzziness can predict the resistance of   
   the algorithm that it is average (by analogy with material resistance,   
   read below to understand), and i think that it is how can be viewed   
   computational complexity.   
      
   Read the rest of my previous thoughts to understand better:   
      
   Yet about what can we take as enough precision and about computational   
   complexity..   
      
      
   As you have just noticed i said before that 2+2=4 is a system that   
   inherently contains enough precision that we call enough precision,   
   because we have to know that it is judged and dictated by our minds of   
   we humans, but when the mind sees a time complexity of n*log(n) or n ,   
   it will measure them by reference to the other time complexities that   
   exists, and we can notice that they are average time complexities if we   
   compare them with an exponential time complexity and with a log(n) time   
   complexity, so this measure dictates that the time complexities of   
   n*log(n) and n are average resistance (by analogy with material   
   resistance, read below to understand), but our minds of humans will also   
   notice that this average resistance is not an exact resistance, so   
   they are missing precision and exactitude, so like the example that i   
   give below of the obese person, we can call the time complexities such   
   as n*log(n) and n as fuzzy and this look like probability calculations.   
      
   Read the rest of all my previous thoughts to understand:   
      
   I will add again more logical rigor to my post about about computational   
   complexity:   
      
   As you have just noticed in my previous post (read below), i said the   
   following:   
      
   That time complexities such as n*log(n) and n are fuzzy.   
      
   But we have to be more logical rigor:   
      
   But what can we take as enough precision ?   
      
   I think that when we say 2+2=4, it has no missing part   
   of precision, so this fact is enough precision,   
   but if we say a time complexity of n*log(n), there is a missing part   
   of precision, because n*log(n) is dependent on reality that needs   
   in this case more precision about an exact precision about the   
   resistance of the algorithm(read below my analogy with material   
   resistance), so this is why we can affirm that time complexities such as   
   n*log(n) and n  are fuzzy, because there is a missing part of precision,   
   but eventhough there is a missing part of precision, there is enough   
   precision that permits to predict that there resistance in reality are   
   average resistance.   
      
   Read the rest of my previous thoughts to understand:   
      
   I correct again one last typo, here is my final post about computational   
   complexity:   
      
   I continu about computational complexity by being more and more   
   rigorous, read again:   
      
   I said previously(read below) that for example the time complexities   
   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   
      
   [continued in next message]   
      
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