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A decision problem H is NP-hard when for every problem L in NP, there is a polynomial-time many-one reduction from L to H. [1]: 80 Another definition is to require that there be a polynomial-time reduction from an NP-complete problem G to H.
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 ...
Informally, an NP-complete problem is an NP problem that is at least as "tough" as any other problem in NP. NP-hard problems are those at least as hard as NP problems; i.e., all NP problems can be reduced (in polynomial time) to them. NP-hard problems need not be in NP; i.e., they need not have solutions verifiable in polynomial time.
The problem has been shown to be NP-hard (more precisely, it is complete for the complexity class FP NP; see function problem), and the decision problem version ("given the costs and a number x, decide whether there is a round-trip route cheaper than x") is NP-complete. The bottleneck travelling salesman problem is also NP-hard.
The maximum independent set problem is NP-hard. However, it can be solved more efficiently than the O(n 2 2 n) time that would be given by a naive brute force algorithm that examines every vertex subset and checks whether it is an independent set. As of 2017 it can be solved in time O(1.1996 n) using polynomial space. [9]
As both n and L grow large, SSP is NP-hard. The complexity of the best known algorithms is exponential in the smaller of the two parameters n and L. The problem is NP-hard even when all input integers are positive (and the target-sum T is a part of the input). This can be proved by a direct reduction from 3SAT. [2]
Graph coloring is computationally hard. It is NP-complete to decide if a given graph admits a k-coloring for a given k except for the cases k ∈ {0,1,2}. In particular, it is NP-hard to compute the chromatic number. [31] The 3-coloring problem remains NP-complete even on 4-regular planar graphs. [32]
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.