In computational complexity theory, the padding argument is a tool to conditionally prove that if some complexity classes are equal, then some other bigger classes are also equal.
The proof that P = NP implies EXP = NEXP uses "padding".
by definition, so it suffices to show .
Let L be a language in NEXP. Since L is in NEXP, there is a non-deterministic Turing machine M that decides L in time for some constant c. Let
where '1' is a symbol not occurring in L. First we show that is in NP, then we will use the deterministic polynomial time machine given by P = NP to show that L is in EXP.
can be decided in non-deterministic polynomial time as follows. Given input , verify that it has the form and reject if it does not. If it has the correct form, simulate M(x). The simulation takes non-deterministic time, which is polynomial in the size of the input, . So, is in NP. By the assumption P = NP, there is also a deterministic machine DM that decides in polynomial time. We can then decide L in deterministic exponential time as follows. Given input , simulate . This takes only exponential time in the size of the input, .
The is called the "padding" of the language L. This type of argument is also sometimes used for space complexity classes, alternating classes, and bounded alternating classes.
In computational complexity theory, a branch of computer science, bounded-error probabilistic polynomial time (BPP) is the class of decision problems solvable by a probabilistic Turing machine in polynomial time with an error probability bounded by 1/3 for all instances. BPP is one of the largest practical classes of problems, meaning most problems of interest in BPP have efficient probabilistic algorithms that can be run quickly on real modern machines. BPP also contains P, the class of problems solvable in polynomial time with a deterministic machine, since a deterministic machine is a special case of a probabilistic machine.
In computational complexity theory, bounded-error quantum polynomial time (BQP) is the class of decision problems solvable by a quantum computer in polynomial time, with an error probability of at most 1/3 for all instances. It is the quantum analogue to the complexity class BPP.
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In theoretical computer science, a nondeterministic Turing machine (NTM) is a theoretical model of computation whose governing rules specify more than one possible action when in some given situations. That is, an NTM's next state is not completely determined by its action and the current symbol it sees, unlike a deterministic Turing machine.
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In theoretical computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to be related by a constant factor.
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