EXPSPACE
Set of decision problems From Wikipedia, the free encyclopedia
In computational complexity theory, EXPSPACE is the set of all decision problems solvable by a deterministic Turing machine in exponential space, i.e., in space, where is a polynomial function of . Some authors restrict to be a linear function, but most authors instead call the resulting class ESPACE. If we use a nondeterministic machine instead, we get the class NEXPSPACE, which is equal to EXPSPACE by Savitch's theorem.
A decision problem is EXPSPACE-complete if it is in EXPSPACE, and every problem in EXPSPACE has a polynomial-time many-one reduction to it. In other words, there is a polynomial-time algorithm that transforms instances of one to instances of the other with the same answer. EXPSPACE-complete problems might be thought of as the hardest problems in EXPSPACE.
EXPSPACE is a strict superset of PSPACE, NP, and P. It contains EXPTIME and is believed to strictly contain it, but this is unproven.
Formal definition
Summarize
Perspective
In terms of DSPACE and NSPACE,
Examples of problems
Formal languages
An example of an EXPSPACE-complete problem is the problem of recognizing whether two regular expressions represent different languages, where the expressions are limited to four operators: union, concatenation, the Kleene star (zero or more copies of an expression), and squaring (two copies of an expression).[1]
Logic
Alur and Henzinger extended linear temporal logic with times (integer) and prove that the validity problem of their logic is EXPSPACE-complete.[2]
Reasoning in the first-order theor of the real numbers with +, ×, = is in EXPSPACE and was conjectured to be EXPSPACE-complete in 1986.[3]
Petri nets
The coverability problem for Petri Nets is EXPSPACE-complete.[4]
The reachability problem for Petri nets was known to be EXPSPACE-hard for a long time,[5] but shown to be nonelementary,[6] so probably not in EXPSPACE. In 2022 it was shown to be Ackermann-complete.[7][8]
See also
References
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