Search results for "Computation complexity"

showing 3 items of 3 documents

Optimal one-shot quantum algorithm for EQUALITY and AND

2017

We study the computation complexity of Boolean functions in the quantum black box model. In this model our task is to compute a function $f:\{0,1\}\to\{0,1\}$ on an input $x\in\{0,1\}^n$ that can be accessed by querying the black box. Quantum algorithms are inherently probabilistic; we are interested in the lowest possible probability that the algorithm outputs incorrect answer (the error probability) for a fixed number of queries. We show that the lowest possible error probability for $AND_n$ and $EQUALITY_{n+1}$ is $1/2-n/(n^2+1)$.

FOS: Computer and information sciencesDiscrete mathematicsOne shotQuantum PhysicsGeneral Computer ScienceProbabilistic logicFOS: Physical sciencesFunction (mathematics)Computational Complexity (cs.CC)Computer Science - Computational ComplexityProbability of errorComputation complexityQuantum algorithmQuantum Physics (quant-ph)Boolean functionQuantumMathematics
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The computational complexity of the criticality problems in a network with interval activity times

2002

Abstract The paper analyzes the criticality in a network with interval activities duration times. A natural generalization of the criticality notion (for a path, an activity and an event) for the case of network with interval activity duration times is given. The computation complexity of five problems linked to the introduced criticality notion is presented.

Discrete mathematicsInformation Systems and ManagementTheoretical computer scienceGeneral Computer ScienceComputational complexity theoryGeneralizationEvent (relativity)Interval (mathematics)Management Science and Operations ResearchIndustrial and Manufacturing EngineeringCriticalityModeling and SimulationPath (graph theory)Computation complexityDuration (project management)MathematicsEuropean Journal of Operational Research
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2014

Large data sets classification is widely used in many industrial applications. It is a challenging task to classify large data sets efficiently, accurately, and robustly, as large data sets always contain numerous instances with high dimensional feature space. In order to deal with this problem, in this paper we present an online Logdet divergence based metric learning (LDML) model by making use of the powerfulness of metric learning. We firstly generate a Mahalanobis matrix via learning the training data with LDML model. Meanwhile, we propose a compressed representation for high dimensional Mahalanobis matrix to reduce the computation complexity in each iteration. The final Mahalanobis mat…

Mahalanobis distanceTraining setApplied MathematicsFeature vectorHigh dimensionalcomputer.software_genreComputation complexityData miningBenchmark dataClassifier (UML)computerAlgorithmAnalysisMathematicsAbstract and Applied Analysis
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