229,065 research outputs found
Nested quantum search and NP-complete problems
A quantum algorithm is known that solves an unstructured search problem in a
number of iterations of order , where is the dimension of the
search space, whereas any classical algorithm necessarily scales as . It
is shown here that an improved quantum search algorithm can be devised that
exploits the structure of a tree search problem by nesting this standard search
algorithm. The number of iterations required to find the solution of an average
instance of a constraint satisfaction problem scales as , with
a constant depending on the nesting depth and the problem
considered. When applying a single nesting level to a problem with constraints
of size 2 such as the graph coloring problem, this constant is
estimated to be around 0.62 for average instances of maximum difficulty. This
corresponds to a square-root speedup over a classical nested search algorithm,
of which our presented algorithm is the quantum counterpart.Comment: 18 pages RevTeX, 3 Postscript figure
nested PLS
In this note we will introduce a class of search problems, called nested
Polynomial Local Search (nPLS) problems, and show that definable NP search
problems, i.e., -definable functions in are characterized
in terms of the nested PLS
The Complexity of Finding Reset Words in Finite Automata
We study several problems related to finding reset words in deterministic
finite automata. In particular, we establish that the problem of deciding
whether a shortest reset word has length k is complete for the complexity class
DP. This result answers a question posed by Volkov. For the search problems of
finding a shortest reset word and the length of a shortest reset word, we
establish membership in the complexity classes FP^NP and FP^NP[log],
respectively. Moreover, we show that both these problems are hard for
FP^NP[log]. Finally, we observe that computing a reset word of a given length
is FNP-complete.Comment: 16 pages, revised versio
A fast, effective local search for scheduling independent jobs in heterogeneous computing environments
The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. Finding optimal schedules for such an environment is (in general) an NP-hard problem, and so heuristic approaches must be used. Work with other NP-hard problems has shown that solutions found by heuristic algorithms can often be improved by applying local search procedures to the solution found. This paper describes a simple but effective local search procedure for scheduling independent jobs in HC environments which, when combined with fast construction heuristics, can find shorter schedules on benchmark problems than other solution techniques found in the literature, and in significantly less time
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