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An example is the partition problem. Both weak NP-hardness and weak polynomial-time correspond to encoding the input agents in binary coding. If a problem is strongly NP-hard, then it does not even have a pseudo-polynomial time algorithm. It also does not have a fully-polynomial time approximation scheme. An example is the 3-partition problem.
An example is the partition problem. Both weak NP-hardness and weak polynomial-time correspond to encoding the input agents in binary coding. If a problem is strongly NP-hard, then it does not even have a pseudo-polynomial time algorithm. It also does not have a fully-polynomial time approximation scheme. An example is the 3-partition problem.
An example is the partition problem. Both weak NP-hardness and weak polynomial-time correspond to encoding the input agents in binary coding. If a problem is strongly NP-hard, then it does not even have a pseudo-polynomial time algorithm. It also does not have a fully-polynomial time approximation scheme. An example is the 3-partition problem.
Such problems arise in approximation algorithms; a famous example is the directed Steiner tree problem, for which there is a quasi-polynomial time approximation algorithm achieving an approximation factor of () (n being the number of vertices), but showing the existence of such a polynomial time algorithm is an open problem.
That is, assuming a solution for H takes 1 unit time, H ' s solution can be used to solve L in polynomial time. [1] [2] As a consequence, finding a polynomial time algorithm to solve a single NP-hard problem would give polynomial time algorithms for all the problems in the complexity class NP.
Pseudo-polynomial time algorithms (4 P) Pages in category "Weakly NP-complete problems" The following 5 pages are in this category, out of 5 total.
If all weights are integers, then the run-time can be improved to (+ ), but the resulting algorithm is only weakly-polynomial. [3] If the weights are integers, and all weights are at most C (where C >1 is some integer), then the problem can be solved in O ( m n log ( n ⋅ C ) ) {\displaystyle O(m{\sqrt {n}}\log(n\cdot C))} weakly ...
NP is the set of decision problems solvable in polynomial time by a nondeterministic Turing machine. NP is the set of decision problems verifiable in polynomial time by a deterministic Turing machine. The first definition is the basis for the abbreviation NP; "nondeterministic, polynomial time". These two definitions are equivalent because the ...