75 research outputs found
Algebraic Characterizations of Complexity-Theoretic Classes of Real Functions
Recursive analysis is the most classical approach to model and discuss computations over the reals. It is usually presented using Type 2 or higher order Turing machines. Recently, it has been shown that computability classes of functions computable in recursive analysis can also be defined (or characterized) in an algebraic machine independent way, without resorting to Turing machines. In particular nice connections between the class of computable functions (and some of its sub- and sup-classes) over the reals and algebraically defined (sub- and sup-) classes of -recursive functions à la Moore 96 have been obtained. However, until now, this has been done only at the computability level, and not at the complexity level. In this paper we provide a framework that allows us to dive into the complexity level of functions over the reals. In particular we provide the first algebraic characterization of polynomial time computable functions over the reals. This framework opens the field of implicit complexity of functions over the reals, and also provide a new reading of some of the existing characterizations at the computability level
Algebraic Characterizations of Complexity-Theoretic Classes of Real Functions
Recursive analysis is the most classical approach to model and discuss computations over the reals. It is usually presented using Type 2 or higher order Turing machines. Recently, it has been shown that computability classes of functions computable in recursive analysis can also be defined (or characterized) in an algebraic machine independent way, without resorting to Turing machines. In particular nice connections between the class of computable functions (and some of its sub- and sup-classes) over the reals and algebraically defined (sub- and sup-) classes of -recursive functions à la Moore 96 have been obtained. However, until now, this has been done only at the computability level, and not at the complexity level. In this paper we provide a framework that allows us to dive into the complexity level of functions over the reals. In particular we provide the first algebraic characterization of polynomial time computable functions over the reals. This framework opens the field of implicit complexity of functions over the reals, and also provide a new reading of some of the existing characterizations at the computability level
Complexity Hierarchies and Higher-order Cons-free Term Rewriting
Constructor rewriting systems are said to be cons-free if, roughly,
constructor terms in the right-hand sides of rules are subterms of the
left-hand sides; the computational intuition is that rules cannot build new
data structures. In programming language research, cons-free languages have
been used to characterize hierarchies of computational complexity classes; in
term rewriting, cons-free first-order TRSs have been used to characterize the
class PTIME.
We investigate cons-free higher-order term rewriting systems, the complexity
classes they characterize, and how these depend on the type order of the
systems. We prove that, for every K 1, left-linear cons-free systems
with type order K characterize ETIME if unrestricted evaluation is used
(i.e., the system does not have a fixed reduction strategy).
The main difference with prior work in implicit complexity is that (i) our
results hold for non-orthogonal term rewriting systems with no assumptions on
reduction strategy, (ii) we consequently obtain much larger classes for each
type order (ETIME versus EXPTIME), and (iii) results for cons-free
term rewriting systems have previously only been obtained for K = 1, and with
additional syntactic restrictions besides cons-freeness and left-linearity.
Our results are among the first implicit characterizations of the hierarchy E
= ETIME ETIME ... Our work confirms prior
results that having full non-determinism (via overlapping rules) does not
directly allow for characterization of non-deterministic complexity classes
like NE. We also show that non-determinism makes the classes characterized
highly sensitive to minor syntactic changes like admitting product types or
non-left-linear rules.Comment: extended version of a paper submitted to FSCD 2016. arXiv admin note:
substantial text overlap with arXiv:1604.0893
Algebraic Characterizations of Complexity-Theoretic Classes of Real Functions
Accepted for publication in International Journal of Unconventional ComputingInternational audienceRecursive analysis is the most classical approach to model and discuss computations over the real numbers.Recently, it has been shown that computability classes of functions in the sense of recursive analysis can be defined (or characterized) in an algebraic machine independent way, without resorting to Turing machines. In particular nice connections between the class of computable functions (and some of its sub- and sup-classes) over the reals and algebraically defined (sub- and sup-) classes of R-recursive functions à la Moore 96 have been obtained. However, until now, this has been done only at the computability level, and not at the complexity level. In this paper we provide a framework that allows us to dive into the complexity level of real functions. In particular we provide the first algebraic characterization of polynomial-time computable functions over the reals. This framework opens the field of implicit complexity of analog functions, and also provides a new reading of some of the existing characterizations at the computability level
Probabilistic Recursion Theory and Implicit Computational Complexity
In this thesis we provide a characterization of
probabilistic computation in itself, from a recursion-theoretical
perspective, without reducing it to deterministic computation.
More specifically, we show that probabilistic computable functions, i.e., those functions which
are computed by Probabilistic Turing Machines (PTM), can be characterized by a natural generalization of Kleene's partial recursive functions which includes, among initial functions,
one that returns identity or successor with probability 1/2. We then prove
the equi-expressivity of the obtained algebra and the class of
functions computed by PTMs.
In the the second part of the thesis we
investigate the relations existing between our recursion-theoretical framework
and sub-recursive classes, in the spirit of Implicit Computational Complexity. More precisely,
endowing predicative recurrence with a random base function is proved
to lead to a characterization of polynomial-time computable
probabilistic functions
On Equivalences, Metrics, and Polynomial Time
International audienceInteractive behaviors are ubiquitous in modern cryptography, but are also present in λ-calculi, in the form of higher-order constructions. Traditionally, however, typed λ-calculi simply do not fit well into cryptography , being both deterministic and too powerful as for the complexity of functions they can express. We study interaction in a λ-calculus for probabilistic polynomial time computable functions. In particular, we show how notions of context equivalence and context metric can both be characterized by way of traces when defined on linear contexts. We then give evidence on how this can be turned into a proof methodology for computational indistinguishability, a key notion in modern cryptography. We also hint at what happens if a more general notion of a context is used
Probabilistic Recursion Theory and Implicit Computational Complexity
We show that probabilistic computable functions, i.e., those func- tions outputting distributions and computed by probabilistic Turing machines, can be characterized by a natural generalization of Church and Kleene’s partial recursive functions. The obtained algebra, following Leivant, can be restricted so as to capture the notion of a polytime sampleable distribution, a key concept in average-case complexity and cryptography
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