Skip to Main content Skip to Navigation
Conference papers

Tailoring Recursion to Characterize Non-Deterministic Complexity Classes Over Arbitrary Structures

Olivier Bournez 1 Felipe Cucker Paulin Jacobé de Naurois 1, 2 Jean-Yves Marion 2
1 PROTHEO - Constraints, automatic deduction and software properties proofs
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 CALLIGRAMME - Linear logic, proof networks and categorial grammars
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We provide machine-independent characterizations of some complexity classes, over an arbitrary structure, in the model of computation proposed by L.Blum, M.Shub and S.Smale. We show that the levels of the polynomial hierarchy correspond to safe recursion with predicative minimization. The levels of the digital polynomial hierarchy correspond to safe recursion with digital predicative minimization. Also, we show that polynomial alternating time corresponds to safe recursion with predicative substitutions and that digital polynomial alternating time corresponds to safe recursion with digital predicative substitutions.
Keywords : bss model implicit complexity complexity
Document type :
Conference papers
Complete list of metadata
https://hal.inria.fr/inria-00000520
Contributor : Olivier Bournez Connect in order to contact the contributor
Submitted on : Wednesday, October 26, 2005 - 7:09:51 PM
Last modification on : Friday, May 13, 2022 - 10:18:05 PM

Identifiers

  • HAL Id : inria-00000520, version 1

Collections

INRIA | CNRS | LORIA | UNIV-LORRAINE | INRIA2

Citation

Olivier Bournez, Felipe Cucker, Paulin Jacobé de Naurois, Jean-Yves Marion. Tailoring Recursion to Characterize Non-Deterministic Complexity Classes Over Arbitrary Structures. In 2nd APPSEM II Workshop - APPSEM'04, Apr 2004, Tallinn/Estonia. ⟨inria-00000520⟩

Export

BibTeX TEI DC DCterms EndNote Datacite

Share

Metrics

Record views

67

 

Contact                    Legal Notice                    Personal Data