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Hence, the Small Set Expansion Hypothesis, which postulates that SSE is hard to approximate, is a stronger (but closely related) assumption than the Unique Game Conjecture. [25] Some approximation problems are known to be SSE-hard [26] (i.e. at least as hard as approximating SSE).
A simple example of an NP-hard problem is the subset sum problem. Informally, if H is NP-hard, then it is at least as difficult to solve as the problems in NP. However, the opposite direction is not true: some problems are undecidable, and therefore even more difficult to solve than all problems in NP, but they are probably not NP-hard (unless ...
The P versus NP problem is a major unsolved problem in theoretical computer science. Informally, it asks whether every problem whose solution can be quickly verified can also be quickly solved. Informally, it asks whether every problem whose solution can be quickly verified can also be quickly solved.
Whether these problems are not decidable in polynomial time is one of the greatest open questions in computer science (see P versus NP ("P = NP") problem for an in-depth discussion). An important notion in this context is the set of NP-complete decision problems, which is a subset of NP and might be informally described as the "hardest ...
It is an NP-hard problem in combinatorial optimization, important in theoretical computer science and operations research. The travelling purchaser problem, the vehicle routing problem and the ring star problem [1] are three generalizations of TSP.
Closely related fields in theoretical computer science are analysis of algorithms and computability theory. A key distinction between analysis of algorithms and computational complexity theory is that the former is devoted to analyzing the amount of resources needed by a particular algorithm to solve a problem, whereas the latter asks a more ...
The W hierarchy is a collection of computational complexity classes. A parameterized problem is in the class W[i], if every instance (,) can be transformed (in fpt-time) to a combinatorial circuit that has weft at most i, such that (,) if and only if there is a satisfying assignment to the inputs that assigns 1 to exactly k inputs.
"A large-scale quantum computer would be able to efficiently solve NP-complete problems." The class of decision problems that can be efficiently solved (in principle) by a fault-tolerant quantum computer is known as BQP. However, BQP is not believed to contain all of NP, and if it does not, then it cannot contain any NP-complete problem. [15]