Effective guessing has unlikely consequences

Funding: EPSRC Grant number EP/P015638/1.A classic result of Paul, Pippenger, Szemeredi and Trotter states that DTIME(n) ⊊ NTIME(n). The natural question then arises: could the inclusion DTIME(t (n)) ⊆ NTIME(n) hold for some superlinear time-constructible function t (n)? If such a function t (n) does exist, then there also exist effective nondeterministic guessing strategies to speed up deterministic computations. In this work, we prove limitations on the effectiveness of nondeterministic guessing to speed up deterministic computations by showing that the existence of effective nondeterministic guessing strategies would have unlikely consequences. In particular, we show that if a subpolynomial amount of nondeterministic guessing could be used to speed up deterministic computation by a polynomial factor, then P ⊊ NTIME(n). Furthermore, even achieving a logarithmic speedup at the cost of making every step nondeterministic would show that SAT ∈ NTIME(n) under appropriate encodings. Of possibly independent interest, under such encodings we also show that SAT can be decided in O(n log n) steps on a nondeterministic multitape Turing machine, improving on the well-known O(n(log n)c) bound for some constant but undetermined exponent c ≥ 1.Publisher PDFPeer reviewe

On the power of real-time turing machines under varying specifications

We investigate the relative computing power of Turing machines with differences in the number of work tapes, heads pro work tape, instruction repertoire etc. We concentrate on the k-tape, k-head and k-head jump models as well as the 2-way multihead finite automata with and without jumps. Differences in computing power between machines of unlike specifications emerge under the real-time restriction. In particular it is shown that k+1 heads are more powerful than k heads for re

Tree-size bounded alternation

AbstractThe size of an accepting computation tree of an alternating Turing machine (ATM) is introduced as a complexity measure. We present a number of applications of tree-size to the study of more traditional complexity classes. Tree-size on ATMs is shown to closely correspond to time on nondeterministic TMs and on nondeterministic auxiliary pushdown automata. One application of the later is a useful new characterization of the class of languages log-space-reducible to context-free languages. Surprising relationships with parallel-time complexity are also demonstrated. ATM computations using at most space S(n) and tree-size Z(n) (simultaneously) can be simulated in alternating space S(n) and time S(n) · log Z(n) (simultaneously). Several well-known simulations, e.g., Savitch's theorem, are special cases of this result. It also leads to improved parallel complexity bounds for many problems in terms of both time and number of “processors.” As one example we show that context-free language recognition in time O(log2 n) is possible on several parallel models. Further, this bound is achievable with only a polynomial number of processors, in contrast to all previously known sub-linear time CFL recognizers

Typically-Correct Derandomization for Small Time and Space

Suppose a language L can be decided by a bounded-error randomized algorithm that runs in space S and time n * poly(S). We give a randomized algorithm for L that still runs in space O(S) and time n * poly(S) that uses only O(S) random bits; our algorithm has a low failure probability on all but a negligible fraction of inputs of each length. As an immediate corollary, there is a deterministic algorithm for L that runs in space O(S) and succeeds on all but a negligible fraction of inputs of each length. We also give several other complexity-theoretic applications of our technique

Elements of computability, decidability, and complexity (Third edition)

These lecture notes are intended to introduce the reader to the basic notions of computability theory, decidability, and complexity. More information on these subjects can be found in classical books such as [Cut80,Dav58,Her69,HoU79,Rog67]. The results reported in these notes are taken from those books and in various parts we closely follow their style of presentation. The reader is encouraged to look at those books for improving his/her knowledge on these topics. Some parts of the chapter on complexity are taken from the lecture notes of a beautiful course given by Prof. Leslie Valiant at Edinburgh University, Scotland, in 1979. It was, indeed, a very stimulating and enjoyable course. For the notions of Predicate Calculus we have used in this book the reader may refer to [Men87]. I would like to thank Dr. Maurizio Proietti at IASI-CNR (Roma, Italy), my colleagues, and my students at the University of Roma Tor Vergata and, in particular, Michele Martone. They have been for me a source of continuous inspiration and enthusiasm. Finally, I would like to thank Dr. Gioacchino Onorati and Lorenzo Costantini of the Aracne Publishing Company for their helpful cooperation

Elements of computability, decidability, and complexity (Third edition)

These lecture notes are intended to introduce the reader to the basic notions of computability theory, decidability, and complexity. More information on these subjects can be found in classical books such as [Cut80,Dav58,Her69,HoU79,Rog67]. The results reported in these notes are taken from those books and in various parts we closely follow their style of presentation. The reader is encouraged to look at those books for improving his/her knowledge on these topics. Some parts of the chapter on complexity are taken from the lecture notes of a beautiful course given by Prof. Leslie Valiant at Edinburgh University, Scotland, in 1979. It was, indeed, a very stimulating and enjoyable course. For the notions of Predicate Calculus we have used in this book the reader may refer to [Men87]. I would like to thank Dr. Maurizio Proietti at IASI-CNR (Roma, Italy), my colleagues, and my students at the University of Roma Tor Vergata and, in particular, Michele Martone. They have been for me a source of continuous inspiration and enthusiasm. Finally, I would like to thank Dr. Gioacchino Onorati and Lorenzo Costantini of the Aracne Publishing Company for their helpful cooperation

Models of quantum computation and quantum programming languages

The goal of the presented paper is to provide an introduction to the basic computational models used in quantum information theory. We review various models of quantum Turing machine, quantum circuits and quantum random access machine (QRAM) along with their classical counterparts. We also provide an introduction to quantum programming languages, which are developed using the QRAM model. We review the syntax of several existing quantum programming languages and discuss their features and limitations.Comment: 23 pages, 10 figures, 9 listing