Arjun Suresh (talk | contribs) |
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<metadesc>NP Complete definition in simple terms. Differences between P, NP, NP Complete and NP Hard Problems</metadesc> | <metadesc>NP Complete definition in simple terms. Differences between P, NP, NP Complete and NP Hard Problems</metadesc> | ||
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It might be because of the name but many graduate students find it difficult to understand <math>NP</math> problems. So, I thought of explaining them in an easy way. (When explanation becomes simple, some points may be lost. So, please do refer standard text books for more information) | It might be because of the name but many graduate students find it difficult to understand <math>NP</math> problems. So, I thought of explaining them in an easy way. (When explanation becomes simple, some points may be lost. So, please do refer standard text books for more information) | ||
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==<math>NP</math> Problems== | ==<math>NP</math> Problems== | ||
| − | Some think <math>NP</math> as Non-Polynomial. But actually | + | Some think <math>NP</math> as Non-Polynomial. But actually it is [https://en.wikipedia.org/wiki/NP_(complexity) Non-deterministic Polynomial time]. i.e.; "yes" instances of these problems can be solved in polynomial time by a non-deterministic Turing machine and hence can take up to exponential time (some problems can be solved in sub-exponential but super polynomial time) by a deterministic Turing machine. In other words these problems can be verified (if a solution is given, say if it is correct or wrong) in polynomial time. Examples include all P problems. One example of a problem not in <math>P</math> but in <math>NP</math> is [https://en.wikipedia.org/wiki/Integer_factorization_problem Integer Factorization]. |
==<math>NP</math> Complete Problems<math>(NPC)</math>== | ==<math>NP</math> Complete Problems<math>(NPC)</math>== | ||
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--[[User:Arjun|Arjun]] ([[User talk:Arjun|talk]]) 22:48, 16 November 2013 (UTC) | --[[User:Arjun|Arjun]] ([[User talk:Arjun|talk]]) 22:48, 16 November 2013 (UTC) | ||
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It might be because of the name but many graduate students find it difficult to understand <math>NP</math> problems. So, I thought of explaining them in an easy way. (When explanation becomes simple, some points may be lost. So, please do refer standard text books for more information)
As the name says these problems can be solved in polynomial time, i.e.; <math>O(n)</math>, <math>O(n^2)</math> or <math>O(n^k)</math>, where <math>k</math> is a constant.
Some think <math>NP</math> as Non-Polynomial. But actually it is Non-deterministic Polynomial time. i.e.; "yes" instances of these problems can be solved in polynomial time by a non-deterministic Turing machine and hence can take up to exponential time (some problems can be solved in sub-exponential but super polynomial time) by a deterministic Turing machine. In other words these problems can be verified (if a solution is given, say if it is correct or wrong) in polynomial time. Examples include all P problems. One example of a problem not in <math>P</math> but in <math>NP</math> is Integer Factorization.
Over the years many problems in <math>NP</math> have been proved to be in <math>P</math> (like Primality Testing). Still, there are many problems in <math>NP</math> not proved to be in <math>P</math>. i.e.; the question still remains whether <math>P = NP</math> (i.e.; whether all <math>NP</math> problems are actually <math>P</math> problems).
<math>NP</math> Complete Problems helps in solving the above question. They are a subset of <math>NP</math> problems with the property that all other <math>NP</math> problems can be reduced to any of them in polynomial time. So, they are the hardest problems in <math>NP</math>, in terms of running time. If it can be showed that any <math>NPC</math> Problem is in <math>P</math>, then all problems in <math>NP</math> will be in <math>P</math> (because of <math>NPC</math> definition), and hence <math>P = NP = NPC</math>.
All <math>NPC</math> problems are in <math>NP</math> (again, due to <math>NPC</math> definition). Examples of <math>NPC</math> problems
These problems need not have any bound on their running time. If any <math>NPC</math> Problem is polynomial time reducible to a problem <math>X</math>, that problem <math>X</math> belongs to <math>NP</math> Hard class. Hence, all <math>NP</math> Complete problems are also <math>NPH</math>. In other words if a <math>NPH</math> problem is non-deterministic polynomial time solvable, its a <math>NPC</math> problem. Example of a <math>NP</math> problem that's not <math>NPC</math> is Halting Problem.
From the diagram, its clear that <math>NPC</math> problems are the hardest problems in <math>NP</math> while being the simplest ones in <math>NPH</math>. i.e.;
$NP ∩ NPH = NPC$
Given a general problem, we can say its in <math>NPC</math>, if and only if we can reduce it to some <math>NP</math> problem (which shows its in NP) and also some <math>NPC</math> problem can be reduced to it (which shows all NP problems can be reduced to this problem).
Also, if a <math>NPH</math> problem is in <math>NP</math>, then it's <math>NPC</math>
--Arjun (talk) 22:48, 16 November 2013 (UTC)
It might be because of the name but many graduate students find it difficult to understand <math>NP</math> problems. So, I thought of explaining them in an easy way. (When explanation becomes simple, some points may be lost. So, please do refer standard text books for more information)
As the name says these problems can be solved in polynomial time, i.e.; <math>O(n)</math>, <math>O(n^2)</math> or <math>O(n^k)</math>, where <math>k</math> is a constant.
Some think <math>NP</math> as Non-Polynomial. But actually its Non-deterministic Polynomial time. i.e.; these problems can be solved in polynomial time by a non-deterministic Turing machine and hence in exponential time by a deterministic Turing machine. In other words these problems can be verified (if a solution is given, say if its correct or wrong) in polynomial time. Examples include all P problems. One example of a problem not in <math>P</math> but in <math>NP</math> is Integer Factorization.
Over the years many problems in <math>NP</math> have been proved to be in <math>P</math> (like Primality Testing). Still, there are many problems in <math>NP</math> not proved to be in <math>P</math>. i.e.; the question still remains whether <math>P = NP</math> (i.e.; whether all <math>NP</math> problems are actually <math>P</math> problems).
<math>NP</math> Complete Problems helps in solving the above question. They are a subset of <math>NP</math> problems with the property that all other <math>NP</math> problems can be reduced to any of them in polynomial time. So, they are the hardest problems in <math>NP</math>, in terms of running time. If it can be showed that any <math>NPC</math> Problem is in <math>P</math>, then all problems in <math>NP</math> will be in <math>P</math> (because of <math>NPC</math> definition), and hence <math>P = NP = NPC</math>.
All <math>NPC</math> problems are in <math>NP</math> (again, due to <math>NPC</math> definition). Examples of <math>NPC</math> problems
These problems need not have any bound on their running time. If any <math>NPC</math> Problem is polynomial time reducible to a problem <math>X</math>, that problem <math>X</math> belongs to <math>NP</math> Hard class. Hence, all <math>NP</math> Complete problems are also <math>NPH</math>. In other words if a <math>NPH</math> problem is non-deterministic polynomial time solvable, its a <math>NPC</math> problem. Example of a <math>NP</math> problem that's not <math>NPC</math> is Halting Problem.
From the diagram, its clear that <math>NPC</math> problems are the hardest problems in <math>NP</math> while being the simplest ones in <math>NPH</math>. i.e.;
$NP ∩ NPH = NPC$
Given a general problem, we can say its in <math>NPC</math>, if and only if we can reduce it to some <math>NP</math> problem (which shows its in NP) and also some <math>NPC</math> problem can be reduced to it (which shows all NP problems can be reduced to this problem).
Also, if a <math>NPH</math> problem is in <math>NP</math>, then it's <math>NPC</math>
--Arjun (talk) 22:48, 16 November 2013 (UTC)
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