25 research outputs found
Dimension Extractors and Optimal Decompression
A *dimension extractor* is an algorithm designed to increase the effective
dimension -- i.e., the amount of computational randomness -- of an infinite
binary sequence, in order to turn a "partially random" sequence into a "more
random" sequence. Extractors are exhibited for various effective dimensions,
including constructive, computable, space-bounded, time-bounded, and
finite-state dimension. Using similar techniques, the Kucera-Gacs theorem is
examined from the perspective of decompression, by showing that every infinite
sequence S is Turing reducible to a Martin-Loef random sequence R such that the
asymptotic number of bits of R needed to compute n bits of S, divided by n, is
precisely the constructive dimension of S, which is shown to be the optimal
ratio of query bits to computed bits achievable with Turing reductions. The
extractors and decompressors that are developed lead directly to new
characterizations of some effective dimensions in terms of optimal
decompression by Turing reductions.Comment: This report was combined with a different conference paper "Every
Sequence is Decompressible from a Random One" (cs.IT/0511074, at
http://dx.doi.org/10.1007/11780342_17), and both titles were changed, with
the conference paper incorporated as section 5 of this new combined paper.
The combined paper was accepted to the journal Theory of Computing Systems,
as part of a special issue of invited papers from the second conference on
Computability in Europe, 200
Bicriteria data compression
The advent of massive datasets (and the consequent design of high-performing
distributed storage systems) have reignited the interest of the scientific and
engineering community towards the design of lossless data compressors which
achieve effective compression ratio and very efficient decompression speed.
Lempel-Ziv's LZ77 algorithm is the de facto choice in this scenario because of
its decompression speed and its flexibility in trading decompression speed
versus compressed-space efficiency. Each of the existing implementations offers
a trade-off between space occupancy and decompression speed, so software
engineers have to content themselves by picking the one which comes closer to
the requirements of the application in their hands. Starting from these
premises, and for the first time in the literature, we address in this paper
the problem of trading optimally, and in a principled way, the consumption of
these two resources by introducing the Bicriteria LZ77-Parsing problem, which
formalizes in a principled way what data-compressors have traditionally
approached by means of heuristics. The goal is to determine an LZ77 parsing
which minimizes the space occupancy in bits of the compressed file, provided
that the decompression time is bounded by a fixed amount (or vice-versa). This
way, the software engineer can set its space (or time) requirements and then
derive the LZ77 parsing which optimizes the decompression speed (or the space
occupancy, respectively). We solve this problem efficiently in O(n log^2 n)
time and optimal linear space within a small, additive approximation, by
proving and deploying some specific structural properties of the weighted graph
derived from the possible LZ77-parsings of the input file. The preliminary set
of experiments shows that our novel proposal dominates all the highly
engineered competitors, hence offering a win-win situation in theory&practice
Constructive Dimension and Turing Degrees
This paper examines the constructive Hausdorff and packing dimensions of
Turing degrees. The main result is that every infinite sequence S with
constructive Hausdorff dimension dim_H(S) and constructive packing dimension
dim_P(S) is Turing equivalent to a sequence R with dim_H(R) <= (dim_H(S) /
dim_P(S)) - epsilon, for arbitrary epsilon > 0. Furthermore, if dim_P(S) > 0,
then dim_P(R) >= 1 - epsilon. The reduction thus serves as a *randomness
extractor* that increases the algorithmic randomness of S, as measured by
constructive dimension.
A number of applications of this result shed new light on the constructive
dimensions of Turing degrees. A lower bound of dim_H(S) / dim_P(S) is shown to
hold for the Turing degree of any sequence S. A new proof is given of a
previously-known zero-one law for the constructive packing dimension of Turing
degrees. It is also shown that, for any regular sequence S (that is, dim_H(S) =
dim_P(S)) such that dim_H(S) > 0, the Turing degree of S has constructive
Hausdorff and packing dimension equal to 1.
Finally, it is shown that no single Turing reduction can be a universal
constructive Hausdorff dimension extractor, and that bounded Turing reductions
cannot extract constructive Hausdorff dimension. We also exhibit sequences on
which weak truth-table and bounded Turing reductions differ in their ability to
extract dimension.Comment: The version of this paper appearing in Theory of Computing Systems,
45(4):740-755, 2009, had an error in the proof of Theorem 2.4, due to
insufficient care with the choice of delta. This version modifies that proof
to fix the error
Constructive dimension and weak truth-table degrees
submitted to Theory of Computing SystemsThis paper examines the constructive Hausdorff and packing dimensions of weak truth-table degrees. The main result is that every infinite sequence with constructive Hausdorff dimension and constructive packing dimension \Dim(S) is weak truth-table equivalent to a sequence with \dim(R) \geq \dim(S) / \Dim(S) - \epsilon, for arbitrary . Furthermore, if \Dim(S) > 0, then \Dim(R) \geq 1 - \epsilon. The reduction thus serves as a \emph{randomness extractor} that increases the algorithmic randomness of , as measured by constructive dimension. A number of applications of this result shed new light on the constructive dimensions of wtt degrees (and, by extension, Turing degrees). A lower bound of \dim(S) / \Dim(S) is shown to hold for the wtt degree of any sequence . A new proof is given of a previously-known zero-one law for the constructive packing dimension of wtt degrees. It is also shown that, for any \emph{regular} sequence (that is, \dim(S) = \Dim(S)) such that , the wtt degree of has constructive Hausdorff and packing dimension equal to 1. Finally, it is shown that no single Turing reduction can be a \emph{universal} constructive Hausdorff dimension extractor, and that \emph{bounded} Turing reductions cannot extract constructive Hausdorff dimension. We also exhibit sequences on which weak truth-table and bounded Turing reductions differ in their ability to extract dimension
Lower bounds on the redundancy in computations from random oracles via betting strategies with restricted wagers
The KuÄeraâGĂĄcs theorem is a landmark result in algorithmic randomness asserting that every real is computable from a Martin-Löf random real. If the computation of the first n bits of a sequence requires n+h(n) bits of the random oracle, then h is the redundancy of the computation. KuÄera implicitly achieved redundancy nlogâĄn while GĂĄcs used a more elaborate coding procedure which achieves redundancy View the MathML source. A similar bound is implicit in the later proof by Merkle and MihailoviÄ. In this paper we obtain optimal strict lower bounds on the redundancy in computations from Martin-Löf random oracles. We show that any nondecreasing computable function g such that ân2âg(n)=â is not a general upper bound on the redundancy in computations from Martin-Löf random oracles. In fact, there exists a real X such that the redundancy g of any computation of X from a Martin-Löf random oracle satisfies ân2âg(n)<â. Moreover, the class of such reals is comeager and includes a View the MathML source real as well as all weakly 2-generic reals. On the other hand, it has been recently shown that any real is computable from a Martin-Löf random oracle with redundancy g, provided that g is a computable nondecreasing function such that ân2âg(n)<â. Hence our lower bound is optimal, and excludes many slow growing functions such as logâĄn from bounding the redundancy in computations from random oracles for a large class of reals. Our results are obtained as an application of a theory of effective betting strategies with restricted wagers which we develop
Optimal redundancy in computations from random oracles
It is a classic result in algorithmic information theory that every infinite binary sequence is computable from an infinite binary sequence which is random in the sense of Martin-Löf. Proved independently by KuËcera [KuËc85] and GĂĄcs [GĂĄc86], this result answered a question by Charles Bennett and has seen numerous applications in the last 30 years. The optimal redundancy in such a coding process has, however, remained unknown. If the computation of the first n bits of a sequence requires n+g(n) bits of the random oracle, then g is the redundancy of the computation. KuËcera implicitly achieved redundancy n log n while GĂĄcs used a more elaborate block-coding procedure which achieved redundancy ân log n. Merkle and MihailoviÂŽc [MM04] provided a different presentation of GĂĄcsâ approach, without improving his redundancy bound. In this paper we devise a new coding method that achieves optimal logarithmic redundancy. For any computable non-decreasing function g such that Pi 2âg(i) is bounded we show that there is a coding process that codes any given infinite binary sequence into a Martin-Löf random infinite binary sequence with redundancy g. This redundancy bound is exponentially smaller than the previous bound of ân log n and is known to be the best possible by recent work [BLPT16], where it was shown that if Pi 2âg(i) diverges then there exists an infinite binary sequence X which cannot be computed by any Martin-Löf random infinite binary sequence with redundancy g. It follows that redundancy Ç« · log n in computation from a random oracle is possible for every infinite binary sequence, if and only if Ç« > 1