# Savitch's theorem

Savitch's theorem In computational complexity theory, Savitch's theorem, proved by Walter Savitch in 1970, gives a relationship between deterministic and non-deterministic space complexity. It states that for any function {displaystyle fin Omega (log(n))} , {displaystyle {mathsf {NSPACE}}left(fleft(nright)right)subseteq {mathsf {DSPACE}}left(fleft(nright)^{2}right).} In other words, if a nondeterministic Turing machine can solve a problem using {displaystyle f(n)} space, a deterministic Turing machine can solve the same problem in the square of that space bound.[1] Although it seems that nondeterminism may produce exponential gains in time, this theorem shows that it has a markedly more limited effect on space requirements.[2] Contents 1 Proof 2 Corollaries 3 See also 4 Notes 5 References 6 External links Proof The proof relies on an algorithm for STCON, the problem of determining whether there is a path between two vertices in a directed graph, which runs in {displaystyle Oleft((log n)^{2}right)} space for {displaystyle n} vertices. The basic idea of the algorithm is to solve recursively a somewhat more general problem, testing the existence of a path from a vertex s to another vertex t that uses at most k edges, where k is a parameter that is given as input to the recursive algorithm. STCON may be solved from this problem by setting k to n. To test for a k-edge path from s to t, one may test whether each vertex u may be the midpoint of the s-t path, by recursively searching for paths of half the length from s to u and u to t. Using pseudocode (in Python syntax) we can express this algorithm as follows: def k_edge_path(s, t, k) -> bool: """k initially equals n (which is the number of vertices)""" if k == 0: return s == t if k == 1: return (s, t) in edges for u in vertices: if k_edge_path(s, u, floor(k / 2)) and k_edge_path(u, t, ceil(k / 2)): return True return False This search calls itself to a recursion depth of {displaystyle O(log n)} levels, each of which requires {displaystyle O(log n)} bits to store the function arguments and local variables at that level: k and all vertices (s, t, u) require {displaystyle O(log n)} bits each. The total auxiliary space complexity is thus {displaystyle Oleft((log n)^{2}right)} .[Note 1] Although described above in the form of a program in a high-level language, the same algorithm may be implemented with the same asymptotic space bound on a Turing machine.

To see why this algorithm implies the theorem, consider the following. For any language {displaystyle Lin {mathsf {NSPACE}}(f(n))} , there is a Turing machine {displaystyle M} which decides {displaystyle L} in space {displaystyle f(n)} . Assume w.l.o.g. the alphabet is a binary alphabet {displaystyle {0,1}} (viz. {displaystyle Lsubseteq {0,1}^{*}} ). For any input word {displaystyle xin {0,1}^{*}} , there is a directed graph {displaystyle G_{x}^{M}} whose vertices are the configurations of {displaystyle M} when running on the input {displaystyle x} . There may be infinitely many such configurations; e.g. when {displaystyle M} keeps writing a symbol on the tape and moving the head to the right in a loop, ad infinitum. The configurations then grow arbitrarily large. However, we know that at most {displaystyle fleft(nright)} space is needed to decide whether {displaystyle xin L} , so we only care about configurations of size at most {displaystyle fleft(nright)} ; call any such configuration admissible. There are finitely many admissible configurations; namely {displaystyle 2^{Oleft(f(n)right)}} . Therefore, the induced subgraph {displaystyle [G_{x}^{M}]} of {displaystyle G_{x}^{M}} containing (exactly) the admissible configurations has {displaystyle 2^{Oleft(f(n)right)}} vertices. For each input {displaystyle xin {0,1}^{n}} , {displaystyle [G_{x}^{M}]} has a path from the starting configuration to an accepting configuration if and only if {displaystyle xin L} . Thus by deciding connectivity in {displaystyle [G_{x}^{M}]} , we can decide membership of {displaystyle x} in {displaystyle L} . By the above algorithm this can be done deterministically in space {displaystyle Oleft(left(log 2^{Oleft(f(n)right)}right)^{2}right)=Oleft(fleft(nright)^{2}right)} ; hence {displaystyle L} is in {displaystyle {mathsf {DSPACE}}left(fleft(nright)^{2}right)} .

Since this holds for all {displaystyle fin Omega (log n)} and all {displaystyle Lin {mathsf {NSPACE}}left(fleft(nright)right)} , we get the statement of the theorem: For all functions {displaystyle fin Omega (log n)} , {displaystyle {mathsf {NSPACE}}left(fleft(nright)right)subseteq {mathsf {DSPACE}}left(fleft(nright)^{2}right)} . ^ Note there can be up to {displaystyle n^{2}} edges in the input graph, each edge consuming {displaystyle 2log n} space, hence the total (i.e., not merely auxiliary) space complexity is {displaystyle O(n^{2}log n)} (and this bound is tight). However, edges may be represented implicitly, via the non-deterministic Turing machine under inspection. Corollaries Some important corollaries of the theorem include: PSPACE = NPSPACE This follows directly from the fact that the square of a polynomial function is still a polynomial function. It is believed that a similar relationship does not exist between the polynomial time complexity classes, P and NP, although this is still an open question. NL ⊆ L2 STCON is NL-complete, and so all languages in NL are also in the complexity class {displaystyle {mathsf {color {Blue}L}}^{2}={mathsf {DSPACE}}left(left(log nright)^{2}right)} . See also Exponential time hypothesis Immerman–Szelepcsényi theorem Notes ^ Arora & Barak (2009) p.86 ^ Arora & Barak (2009) p.92 References Arora, Sanjeev; Barak, Boaz (2009), Computational complexity. A modern approach, Cambridge University Press, ISBN 978-0-521-42426-4, Zbl 1193.68112 Papadimitriou, Christos (1993), "Section 7.3: The Reachability Method", Computational Complexity (1st ed.), Addison Wesley, pp. 149–150, ISBN 0-201-53082-1 Savitch, Walter J. (1970), "Relationships between nondeterministic and deterministic tape complexities", Journal of Computer and System Sciences, 4 (2): 177–192, doi:10.1016/S0022-0000(70)80006-X, hdl:10338.dmlcz/120475 Sipser, Michael (1997), "Section 8.1: Savitch's Theorem", Introduction to the Theory of Computation, PWS Publishing, pp. 279–281, ISBN 0-534-94728-X External links Lance Fortnow, Foundations of Complexity, Lesson 18: Savitch's Theorem. Accessed 09/09/09. Richard J. Lipton, Savitch’s Theorem. Gives a historical account on how the proof was discovered. Categories: Structural complexity theoryTheorems in computational complexity theory

Si quieres conocer otros artículos parecidos a Savitch's theorem puedes visitar la categoría Structural complexity theory.

Subir

Utilizamos cookies propias y de terceros para mejorar la experiencia de usuario Más información