Some programming languages for LOGSPACE and PTIME

We propose two characterizations of complexity classes by means of programming languages. The first concerns Logspace while the second leads to Ptime. This latter characterization shows that adding a choice command to a Ptime language (the language WHILE of Jones) may not necessarily provide NPtime computations. The result is close to Cook who used “auxiliary push-down automata”. Logspace is obtained through a decidable mechanism of tiering. It is based on an analysis of deforestation due to Wadler in. We get also a characterization of NLogspace

Observation of implicit complexity by non confluence

We propose to consider non confluence with respect to implicit complexity. We come back to some well known classes of first-order functional program, for which we have a characterization of their intentional properties, namely the class of cons-free programs, the class of programs with an interpretation, and the class of programs with a quasi-interpretation together with a termination proof by the product path ordering. They all correspond to PTIME. We prove that adding non confluence to the rules leads to respectively PTIME, NPTIME and PSPACE. Our thesis is that the separation of the classes is actually a witness of the intentional properties of the initial classes of programs

Memoization for Unary Logic Programming: Characterizing PTIME

We give a characterization of deterministic polynomial time computation based on an algebraic structure called the resolution semiring, whose elements can be understood as logic programs or sets of rewriting rules over first-order terms. More precisely, we study the restriction of this framework to terms (and logic programs, rewriting rules) using only unary symbols. We prove it is complete for polynomial time computation, using an encoding of pushdown automata. We then introduce an algebraic counterpart of the memoization technique in order to show its PTIME soundness. We finally relate our approach and complexity results to complexity of logic programming. As an application of our techniques, we show a PTIME-completeness result for a class of logic programming queries which use only unary function symbols.Comment: Soumis {\`a} LICS 201

Computability and Complexity from a Programming Perspective (MFPS Draft preview)

AbstractThe author's forthcoming book proves central results in computability and complexity theory from a programmer-oriented perspective. In addition to giving more natural definitions, proofs and perspectives on classical theorems by Cook, Hartmanis, Savitch, etc., some new results have come from the alternative approach.One: for a computation model more natural than the Turing machine, multiplying the available problem-solving time provably increases problem-solving power (in general not true for Turing machines). Another: the class of decision problems solvable by Wadler's “treeless” programs [8], or by cons-free programs on Lisp-like lists, are identical with the well-studied complexity class LOGSPACE.A third is that cons-free programs augmented with recursion can solve all and only PTIME problems. Paradoxically, these programs often run in exponential time (not a contradiction, since they can be simulated in polynomial time by memoization.) This tradeoff indicates a tension between running time and memory space which seems worth further investigation

Unification and Logarithmic Space

We present an algebraic characterization of the complexity classes Logspace and Nlogspace, using an algebra with a composition law based on unification. This new bridge between unification and complexity classes is rooted in proof theory and more specifically linear logic and geometry of interaction. We show how to build a model of computation in the unification algebra and then, by means of a syntactic representation of finite permutations in the algebra, we prove that whether an observation (the algebraic counterpart of a program) accepts a word can be decided within logarithmic space. Finally, we show that the construction naturally corresponds to pointer machines, a convenient way of understanding logarithmic space computation.Comment: arXiv admin note: text overlap with arXiv:1402.432