Révision 139

CharacterizingPH/draft.tex (revision 139)
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\section{Introduction and motivation}
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Today, there are countless approaches towards characterising complexity classes via logic.
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Foremost amongst these lies the proof-theoretic approach, characterising classes as the `representable' functions of some logic or theory.
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Examples include bounded arithmetic \cite{Buss86book} \cite{Krajicek:1996:BAP:225488} \cite{Cook:2010:LFP:1734064}, applicative theories \cite{Cantini02} \cite{KahOit:13:ph-levels}, intrinsic and ramified theories \cite{Leivant94:intrinsic-theories} \cite{BelHof:02}, fragments of linear logic \cite{Girard94:lll} \cite{Lafont04} \cite{Baillot15} and fragments of intuitionistic logic \cite{Leivant94:found-delin-ptime}.
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Examples include bounded arithmetic \cite{Buss86book} \cite{Krajicek:1996:BAP:225488} \cite{Cook:2010:LFP:1734064}, applicative theories \cite{Cantini02} \cite{KahOit:13:ph-levels}, intrinsic and ramified theories \cite{Leivant94:intrinsic-theories} \cite{BelHof:02}, fragments of linear logic \cite{GirardSS92:bounded-ll} \cite{Girard94:lll} \cite{Lafont04} \cite{Baillot15} and fragments of intuitionistic logic \cite{Leivant94:found-delin-ptime}.
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To some extent there is a distinction between various notions of `representability', namely between logics that type terms computing functions of a given complexity class, and theories that prove the totality of functions in a given complexity class.


  		





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Perhaps a more important (and somewhat orthogonal) distinction for the \textsc{Dice-Fopara} community is whether the constraints on the logic or theory are \emph{implicit} or \emph{explicit}. The former includes constraints such as ramification, type level and substructural considerations, while the latter includes bounded quantification, bounded modalities (e.g. in bounded linear logic) etc. This distinction is also naturally exhibited in associated function algebras, e.g. Cobham vs. Bellantoni-Cook \cite{Cobham,BellantoniCook92}.


  		





  
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To some extent there is a distinction between various notions of `representability', namely between logics that type terms computing functions of a given complexity class, and theories that prove the \emph{totality} or \emph{convergence} of programs computing functions in a given complexity class.


  		





  
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Perhaps a more important (and somewhat orthogonal) distinction for the \textsc{Dice-Fopara} community is whether the constraints on the logic or theory are \emph{implicit} or \emph{explicit}. The former includes constraints such as ramification, type level and substructural considerations, while the latter includes bounded quantification, e.g.\ \cite{Buss86book}, bounded modalities, e.g.\ \cite{GirardSS92:bounded-ll} etc. This distinction is also naturally exhibited in associated function algebras, e.g. Cobham's \emph{limited} recursion on notation \cite{Cobham} vs.\ Bellantoni and Cook's \emph{predicative} recursion on notation \cite{BellantoniCook92}.


  		





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Some correlations abound: explicit bounds are typically far more useful for more fine-grained characterisations of complexity classes, e.g.\ levels of the polynomial or arithmetical hierarchies (Buss, Mints), and often admit witness extraction methods that remain in a ground type programming language, e.g.\ function algebras. Implicit bounds, however, are more often associated with higher-typed programming languages, which are arguably more useful for achieving witness extraction at all for powerful theories such as arithmetic and set theory. Complexity bounds are harder to obtain, but the framework is nonetheless somewhat more desirable since no bounds occur per se in the characterisation itself.


  		





  
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Some correlations abound: explicit bounds are typically far more useful for more fine-grained characterisations of complexity classes, e.g.\ levels of the polynomial or arithmetical hierarchies, and often admit witness extraction methods that remain in a ground type programming language, e.g.\ via recursion theoretic characterisations. Implicit bounds, however, are more often associated with higher-typed programming languages, which are arguably more useful for achieving witness extraction at all for powerful theories such as arithmetic and set theory. Complexity bounds are harder to obtain, but the framework is nonetheless somewhat more desirable since no bounds occur in the characterisation itself \emph{per se}.


  		





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In this line of work we attempt to ameliorate the situation by using implicit methods to delineate fine-grained hierarchies of feasible complexity classes, namely the polynomial hierarchy.


  		





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One particular feature of this work that helps make this possible is to break one of the aforementioned correlations: while we use implicit constraints, our witness extraction methods will use only functions of bounded type level. In this way we can naturally appeal to function algebras, which are of ground type in nature, which implicitly characterise PH, namely Bellantoni's extension of safe recursion by safe minimisation \cite{BellantoniThesis}.


  		





  
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In this line of work we attempt to ameliorate the situation by using implicit methods to delineate fine-grained hierarchies of feasible complexity classes, namely the \emph{polynomial hierarchy}, $\ph$.


  		





  
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One particular feature of this work that helps make this possible is to break one of the aforementioned correlations: while we use implicit constraints, our witness extraction methods will use only functions of bounded type level. In this way we can naturally appeal to function algebras, which are of ground type in nature, which implicitly characterise $\ph$, namely via \emph{predicative minimisation} \cite{BellantoniThesis}.


  		





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In order to remain in this class of programs and not get lost with higher types, we appeal to the \emph{witness function method} of extracting programs from proofs, a technique developed by Buss (and independently Mints) which is ideal for extracting ground programs directly from classical proofs in weak theories.


  		





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This extends work presented in \cite{BaillotDas16}. %at \emph{CSL '16}.


  		





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\section{State of the art}


  		





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As we have already argued, it is natural to expect that characterisations of hierarchies such as $\ph$ are more readily established by using ground or bounded type witness extraction procedures, due to the correspondence between logical searches in a program and the quantification over objects of ground type in a theory.


  		





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As justification for this position, consider the following table of examples of known characterisations, distinguished by the type level of programs extracted:


  		





  
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As justification for this position, consider the following table of examples of known characterisations:


  		





  
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%, distinguished by the type level of programs extracted:


  		





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\bigskip


  		





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