Search results for "D algorithm"

showing 10 items of 327 documents

Quantum algorithm for tree size estimation, with applications to backtracking and 2-player games

2017

We study quantum algorithms on search trees of unknown structure, in a model where the tree can be discovered by local exploration. That is, we are given the root of the tree and access to a black box which, given a vertex $v$, outputs the children of $v$. We construct a quantum algorithm which, given such access to a search tree of depth at most $n$, estimates the size of the tree $T$ within a factor of $1\pm \delta$ in $\tilde{O}(\sqrt{nT})$ steps. More generally, the same algorithm can be used to estimate size of directed acyclic graphs (DAGs) in a similar model. We then show two applications of this result: a) We show how to transform a classical backtracking search algorithm which exam…

FOS: Computer and information sciencesQuantum PhysicsSpeedupBacktrackingFOS: Physical sciences0102 computer and information sciences02 engineering and technologyComputational Complexity (cs.CC)Directed acyclic graph01 natural sciencesSearch treeCombinatoricsComputer Science - Computational Complexity010201 computation theory & mathematicsSearch algorithm020204 information systemsComputer Science - Data Structures and AlgorithmsTernary search tree0202 electrical engineering electronic engineering information engineeringData Structures and Algorithms (cs.DS)Quantum algorithmDepth-first searchQuantum Physics (quant-ph)MathematicsProceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing
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Quantum Algorithm for Dynamic Programming Approach for DAGs. Applications for Zhegalkin Polynomial Evaluation and Some Problems on DAGs

2018

In this paper, we present a quantum algorithm for dynamic programming approach for problems on directed acyclic graphs (DAGs). The running time of the algorithm is $O(\sqrt{\hat{n}m}\log \hat{n})$, and the running time of the best known deterministic algorithm is $O(n+m)$, where $n$ is the number of vertices, $\hat{n}$ is the number of vertices with at least one outgoing edge; $m$ is the number of edges. We show that we can solve problems that use OR, AND, NAND, MAX and MIN functions as the main transition steps. The approach is useful for a couple of problems. One of them is computing a Boolean formula that is represented by Zhegalkin polynomial, a Boolean circuit with shared input and non…

FOS: Computer and information sciencesQuantum PhysicsTheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITYComputer Science - Data Structures and AlgorithmsFOS: Physical sciencesData Structures and Algorithms (cs.DS)Quantum Physics (quant-ph)MathematicsofComputing_DISCRETEMATHEMATICS
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Constructing Antidictionaries in Output-Sensitive Space

2021

A word $x$ that is absent from a word $y$ is called minimal if all its proper factors occur in $y$. Given a collection of $k$ words $y_1,y_2,\ldots,y_k$ over an alphabet $\Sigma$, we are asked to compute the set $\mathrm{M}^{\ell}_{y_{1}\#\ldots\#y_{k}}$ of minimal absent words of length at most $\ell$ of word $y=y_1\#y_2\#\ldots\#y_k$, $\#\notin\Sigma$. In data compression, this corresponds to computing the antidictionary of $k$ documents. In bioinformatics, it corresponds to computing words that are absent from a genome of $k$ chromosomes. This computation generally requires $\Omega(n)$ space for $n=|y|$ using any of the plenty available $\mathcal{O}(n)$-time algorithms. This is because a…

FOS: Computer and information sciencesSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniOutput sensitive algorithmsString algorithmsPhysicsAntidictionarieSettore INF/01 - InformaticaOutput sensitive algorithm0102 computer and information sciencesAbsent wordsSpace (mathematics)01 natural sciencesAntidictionariesCombinatorics010201 computation theory & mathematicsTheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITYData compressionComputer Science - Data Structures and AlgorithmsData Structures and Algorithms (cs.DS)Computer Science::Symbolic Computation[INFO]Computer Science [cs]Absent wordAlphabetWord (group theory)2019 Data Compression Conference (DCC)
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String attractors and combinatorics on words

2019

The notion of \emph{string attractor} has recently been introduced in [Prezza, 2017] and studied in [Kempa and Prezza, 2018] to provide a unifying framework for known dictionary-based compressors. A string attractor for a word $w=w[1]w[2]\cdots w[n]$ is a subset $\Gamma$ of the positions $\{1,\ldots,n\}$, such that all distinct factors of $w$ have an occurrence crossing at least one of the elements of $\Gamma$. While finding the smallest string attractor for a word is a NP-complete problem, it has been proved in [Kempa and Prezza, 2018] that dictionary compressors can be interpreted as algorithms approximating the smallest string attractor for a given word. In this paper we explore the noti…

FOS: Computer and information sciencesSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaFormal Languages and Automata Theory (cs.FL)De Brujin wordComputer Science - Formal Languages and Automata TheoryBurrows-Wheeler transformString attractorComputer Science - Data Structures and AlgorithmsThue-Morse wordLempel-Ziv encodingBurrows-Wheeler transform; De Brujin word; Lempel-Ziv encoding; Run-length encoding; String attractor; Thue-Morse wordData Structures and Algorithms (cs.DS)Run-length encoding
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A quantum vocal theory of sound

2020

Concepts and formalism from acoustics are often used to exemplify quantum mechanics. Conversely, quantum mechanics could be used to achieve a new perspective on acoustics, as shown by Gabor studies. Here, we focus in particular on the study of human voice, considered as a probe to investigate the world of sounds. We present a theoretical framework that is based on observables of vocal production, and on some measurement apparati that can be used both for analysis and synthesis. In analogy to the description of spin states of a particle, the quantum-mechanical formalism is used to describe the relations between the fundamental states associated with phonetic labels such as phonation, turbule…

FOS: Computer and information sciencesSound (cs.SD)Computer scienceAudio processingAnalogyAudio processing; Quantum-inspired algorithms; Sound representation01 natural sciencesComputer Science - Sound050105 experimental psychologyTheoretical Computer Sciencesymbols.namesakeAudio and Speech Processing (eess.AS)0103 physical sciencesFOS: Electrical engineering electronic engineering information engineering0501 psychology and cognitive sciencesPhonationElectrical and Electronic Engineering010306 general physicsQuantumHuman voiceQuantum computerSound representationSettore INF/01 - Informatica05 social sciencesStatistical and Nonlinear PhysicsObservableSettore MAT/04 - Matematiche ComplementariElectronic Optical and Magnetic MaterialsVibrationClassical mechanicsFourier transformComputer Science::SoundModeling and SimulationSignal ProcessingsymbolsQuantum-inspired algorithms Audio processing Sound representationQuantum-inspired algorithmsSettore ING-INF/05 - Sistemi di Elaborazione delle InformazioniElectrical Engineering and Systems Science - Audio and Speech Processing
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Large-scale compression of genomic sequence databases with the Burrows-Wheeler transform

2012

Motivation The Burrows-Wheeler transform (BWT) is the foundation of many algorithms for compression and indexing of text data, but the cost of computing the BWT of very large string collections has prevented these techniques from being widely applied to the large sets of sequences often encountered as the outcome of DNA sequencing experiments. In previous work, we presented a novel algorithm that allows the BWT of human genome scale data to be computed on very moderate hardware, thus enabling us to investigate the BWT as a tool for the compression of such datasets. Results We first used simulated reads to explore the relationship between the level of compression and the error rate, the leng…

FOS: Computer and information sciencesStatistics and ProbabilityBurrows–Wheeler transformComputer scienceData_CODINGANDINFORMATIONTHEORYBurrows-Wheeler transformcomputer.software_genreBiochemistryBurrows-Wheeler transform; Data Compression; Next-generation sequencingComputer Science - Data Structures and AlgorithmsEscherichia coliCode (cryptography)HumansOverhead (computing)Data Structures and Algorithms (cs.DS)Computer SimulationQuantitative Biology - GenomicsMolecular BiologyGenomics (q-bio.GN)Genome HumanString (computer science)Search engine indexingSortingGenomicsSequence Analysis DNAConstruct (python library)Data CompressionComputer Science ApplicationsComputational MathematicsComputational Theory and MathematicsFOS: Biological sciencesNext-generation sequencingData miningDatabases Nucleic AcidcomputerAlgorithmsData compression
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Binary jumbled string matching for highly run-length compressible texts

2012

The Binary Jumbled String Matching problem is defined as: Given a string $s$ over $\{a,b\}$ of length $n$ and a query $(x,y)$, with $x,y$ non-negative integers, decide whether $s$ has a substring $t$ with exactly $x$ $a$'s and $y$ $b$'s. Previous solutions created an index of size O(n) in a pre-processing step, which was then used to answer queries in constant time. The fastest algorithms for construction of this index have running time $O(n^2/\log n)$ [Burcsi et al., FUN 2010; Moosa and Rahman, IPL 2010], or $O(n^2/\log^2 n)$ in the word-RAM model [Moosa and Rahman, JDA 2012]. We propose an index constructed directly from the run-length encoding of $s$. The construction time of our index i…

FOS: Computer and information sciencesString algorithmsStructure (category theory)Binary numberG.2.1Data_CODINGANDINFORMATIONTHEORY0102 computer and information sciences02 engineering and technologyString searching algorithm01 natural sciencesComputer Science - Information RetrievalTheoretical Computer ScienceCombinatoricsdata structuresSimple (abstract algebra)Computer Science - Data Structures and AlgorithmsString algorithms; jumbled pattern matching; prefix normal form; data structures0202 electrical engineering electronic engineering information engineeringParikh vectorData Structures and Algorithms (cs.DS)Run-length encodingMathematics68W32 68P05 68P20String (computer science)prefix normal formSubstringComputer Science Applicationsjumbled pattern matching010201 computation theory & mathematicsData structureSignal ProcessingRun-length encoding020201 artificial intelligence & image processingConstant (mathematics)Information Retrieval (cs.IR)Information SystemsInformation Processing Letters
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Forrelation

2014

We achieve essentially the largest possible separation between quantum and classical query complexities. We do so using a property-testing problem called Forrelation, where one needs to decide whether one Boolean function is highly correlated with the Fourier transform of a second function. This problem can be solved using 1 quantum query, yet we show that any randomized algorithm needs Ω(√(N)log(N)) queries (improving an Ω(N[superscript 1/4]) lower bound of Aaronson). Conversely, we show that this 1 versus Ω(√(N)) separation is optimal: indeed, any t-query quantum algorithm whatsoever can be simulated by an O(N[superscript 1-1/2t])-query randomized algorithm. Thus, resolving an open questi…

FOS: Computer and information sciencesTheoretical computer scienceGeneral Computer ScienceComputational complexity theoryComputer scienceGeneralizationGeneral MathematicsSeparation (aeronautics)FOS: Physical sciences0102 computer and information sciencesComputational Complexity (cs.CC)01 natural sciencesUpper and lower boundsCombinatorics0103 physical sciences010306 general physicsBoolean functionQuantumComputer Science::DatabasesQuantum computerMathematicsDiscrete mathematicsQuantum PhysicsFunction (mathematics)Randomized algorithmComputer Science - Computational Complexity010201 computation theory & mathematicsQuantum algorithmQuantum Physics (quant-ph)Proceedings of the forty-seventh annual ACM symposium on Theory of Computing
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Time and space efficient quantum algorithms for detecting cycles and testing bipartiteness

2016

We study space and time efficient quantum algorithms for two graph problems -- deciding whether an $n$-vertex graph is a forest, and whether it is bipartite. Via a reduction to the s-t connectivity problem, we describe quantum algorithms for deciding both properties in $\tilde{O}(n^{3/2})$ time and using $O(\log n)$ classical and quantum bits of storage in the adjacency matrix model. We then present quantum algorithms for deciding the two properties in the adjacency array model, which run in time $\tilde{O}(n\sqrt{d_m})$ and also require $O(\log n)$ space, where $d_m$ is the maximum degree of any vertex in the input graph.

FOS: Computer and information sciencesVertex (graph theory)Quantum PhysicsNuclear and High Energy PhysicsReduction (recursion theory)Two-graphFOS: Physical sciencesGeneral Physics and AstronomyStatistical and Nonlinear PhysicsTheoretical Computer ScienceCombinatoricsComputational Theory and MathematicsComputer Science - Data Structures and AlgorithmsBipartite graphGraph (abstract data type)Adjacency listData Structures and Algorithms (cs.DS)Quantum algorithmAdjacency matrixQuantum Physics (quant-ph)Mathematical PhysicsMathematicsofComputing_DISCRETEMATHEMATICSMathematicsQuantum Information and Computation
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Hybrid Genetic Algorithms in Data Mining Applications

2009

Genetic algorithms (GAs) are a class of problem solving techniques which have been successfully applied to a wide variety of hard problems (Goldberg, 1989). In spite of conventional GAs are interesting approaches to several problems, in which they are able to obtain very good solutions, there exist cases in which the application of a conventional GA has shown poor results. Poor performance of GAs completely depends on the problem. In general, problems severely constrained or problems with difficult objective functions are hard to be optimized using GAs. Regarding the difficulty of a problem for a GA there is a well established theory. Traditionally, this has been studied for binary encoded …

Fitness functionComputer scienceHybrid genetic algorithmsSimulated annealingGenetic algorithmData miningcomputer.software_genrecomputerTabu searchFSA-Red Algorithm
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