Search results for " optimization."

showing 10 items of 2333 documents

Separatrix reconstruction to identify tipping points in an eco-epidemiological model

2018

Many ecological systems exhibit tipping points such that they suddenly shift from one state to another. These shifts can be devastating from an ecological point of view, and additionally have severe implications for the socio-economic system. They can be caused by overcritical perturbations of the state variables such as external shocks, disease emergence, or species removal. It is therefore important to be able to quantify the tipping points. Here we present a study of the tipping points by considering the basins of attraction of the stable equilibrium points. We address the question of finding the tipping points that lie on the separatrix surface, which partitions the space of system traj…

State variableMathematical optimizationRadial basis functionComputer scienceSeparatrixApplied MathematicsStable equilibriumComputational mathematics010103 numerical & computational mathematicsDynamical systemDynamical system01 natural sciences010101 applied mathematicsRegime shiftComputational MathematicsGroup huntingSettore MAT/08 - Analisi NumericaMoving Least Squares approximationAllee threshold; Dynamical system; Group hunting; Moving Least Squares approximation; Radial basis function; Regime shift; Computational Mathematics; Applied MathematicsRegime shiftPoint (geometry)Statistical physics0101 mathematicsMoving least squaresAllee threshold
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Observer-based control design for a class of nonlinear systems subject to unknown inputs: LMI approach

2015

This paper deals with the problem of observer-based controller design for a class of nonlinear systems subject to unknown inputs. A novel method is presented to design a controller using estimated state variables which guarantees all the state variables of the closed-loop system converge to the vicinity of the origin and stay there forever. This is done via satisfying several sufficient conditions in terms of nonlinear matrix inequalities. In light of linear algebra, particularly matrix decompositions, the achieved conditions will be converted to a Linear Matrix Inequality (LMI) problem to facilitate the procedure of computing the observer and controller gains. Finally, the effectiveness of…

State-transition matrixMathematical optimizationState variableObserver (quantum physics)ChaoticLinear matrix inequalityNonlinear systemControl theory[INFO.INFO-AU]Computer Science [cs]/Automatic Control EngineeringLinear algebraObserver based[INFO.INFO-AU] Computer Science [cs]/Automatic Control EngineeringComputingMilieux_MISCELLANEOUSMathematics
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Voltage Profile Improvement for Soc Son's Low-Voltage Grid with High Penetration of PV Systems by Optimizing the Location of SVC Devices

2018

This paper presents a method that is applied to optimize the placement of Static VAR compensators in a real low-voltage grid in the Vietnamese territory. In this way, the voltage profile of the distribution grid turns to be improved. A heuristic method, the Binary Particle Swarm Optimization, is used to find a solution to this problem within the Matlab environment. A case study that considers a high penetration of rooftop PV systems in a branch of Soc Son distribution grid is implemented to show the efficiency of the optimization method for this specific application.

Static VAr compensators Reactive power Voltage control Renewable energy sources Particle swarm optimization OptimizationComputer sciencebusiness.industry020209 energyPhotovoltaic systemParticle swarm optimization02 engineering and technologyPenetration (firestop)AC powerGridRenewable energySettore ING-IND/33 - Sistemi Elettrici Per L'Energia0202 electrical engineering electronic engineering information engineeringElectronic engineeringbusinessMATLABcomputercomputer.programming_languageVoltage2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
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Transitions between imperfectly ordered crystalline structures: A phase switch Monte Carlo study

2012

A model for two-dimensional colloids confined laterally by ``structured boundaries'' (i.e., ones that impose a periodicity along the slit) is studied by Monte Carlo simulations. When the distance $D$ between the confining walls is reduced at constant particle number from an initial value ${D}_{0}$, for which a crystalline structure commensurate with the imposed periodicity fits, to smaller values, a succession of phase transitions to imperfectly ordered structures occur. These structures have a reduced number of rows parallel to the boundaries (from $n$ to $n\ensuremath{-}1$ to $n\ensuremath{-}2$, etc.) and are accompanied by an almost periodic strain pattern, due to ``soliton staircases'' …

Statistical ensemblePhase transitionMathematical optimizationStatistical Mechanics (cond-mat.stat-mech)Monte Carlo methodPhase (waves)Thermodynamic integrationFOS: Physical sciencesStatistical mechanicsOrders of magnitude (time)Statistical physicsEnergy (signal processing)Condensed Matter - Statistical MechanicsMathematics
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A Comparison of Formulae for Calculating Cost-Efficient Sample Sizes of Case-Control Studies with an Internal Validation Scheme

2000

When a case-control study is planned to include an internal validation study, the sample size of the study and the proportion of validated observations has to be calculated. There are a variety of alternative methods to accomplish this. In this article some possible procedures will be compared in order to clarify whether considerable differences in the suggested optimal designs occur, dependent on the used method.

Statistics and ProbabilityAlternative methodsScheme (programming language)Optimal designMathematical optimizationCost efficiencyEstimation theoryComputer scienceSmall sampleGeneral MedicineSample size determinationStatisticsStatistics Probability and UncertaintyInternal validationcomputercomputer.programming_languageBiometrical Journal
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Asymptotic optimality of myopic information-based strategies for Bayesian adaptive estimation

2016

This paper presents a general asymptotic theory of sequential Bayesian estimation giving results for the strongest, almost sure convergence. We show that under certain smoothness conditions on the probability model, the greedy information gain maximization algorithm for adaptive Bayesian estimation is asymptotically optimal in the sense that the determinant of the posterior covariance in a certain neighborhood of the true parameter value is asymptotically minimal. Using this result, we also obtain an asymptotic expression for the posterior entropy based on a novel definition of almost sure convergence on "most trials" (meaning that the convergence holds on a fraction of trials that converge…

Statistics and ProbabilityAsymptotic analysisMathematical optimizationPosterior probabilityBayesian probabilityMathematics - Statistics TheoryStatistics Theory (math.ST)050105 experimental psychologydifferential entropyDifferential entropyactive data selection03 medical and health sciences0302 clinical medicineactive learningFOS: Mathematics0501 psychology and cognitive sciencescost of observationdecision theoryMathematicsD-optimalityBayes estimatorSequential estimation05 social sciencesBayesian adaptive estimationAsymptotically optimal algorithmConvergence of random variablesasymptotic optimalitysequential estimation030217 neurology & neurosurgery
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Hölder Continuity up to the Boundary of Minimizers for Some Integral Functionals with Degenerate Integrands

2007

We study qualitative properties of minimizers for a class of integral functionals, defined in a weighted space. In particular we obtain Hölder regularity up to the boundary for the minimizers of an integral functional of high order by using an interior local regularity result and a modified Moser method with special test function.

Statistics and ProbabilityClass (set theory)Article Subjectlcsh:MathematicsApplied MathematicsMathematical analysisDegenerate energy levelsBoundary (topology)Hölder conditionlcsh:QA1-939Modeling and SimulationTest functions for optimizationlcsh:QHigh orderlcsh:ScienceWeighted spaceMathematicsJournal of Applied Mathematics and Stochastic Analysis
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A fast and recursive algorithm for clustering large datasets with k-medians

2012

Clustering with fast algorithms large samples of high dimensional data is an important challenge in computational statistics. Borrowing ideas from MacQueen (1967) who introduced a sequential version of the $k$-means algorithm, a new class of recursive stochastic gradient algorithms designed for the $k$-medians loss criterion is proposed. By their recursive nature, these algorithms are very fast and are well adapted to deal with large samples of data that are allowed to arrive sequentially. It is proved that the stochastic gradient algorithm converges almost surely to the set of stationary points of the underlying loss criterion. A particular attention is paid to the averaged versions, which…

Statistics and ProbabilityClustering high-dimensional dataFOS: Computer and information sciencesMathematical optimizationhigh dimensional dataMachine Learning (stat.ML)02 engineering and technologyStochastic approximation01 natural sciencesStatistics - Computation010104 statistics & probabilityk-medoidsStatistics - Machine Learning[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]stochastic approximation0202 electrical engineering electronic engineering information engineeringComputational statisticsrecursive estimatorsAlmost surely[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]0101 mathematicsCluster analysisComputation (stat.CO)Mathematicsaveragingk-medoidsRobbins MonroApplied MathematicsEstimator[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]stochastic gradient[ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]MedoidComputational MathematicsComputational Theory and Mathematicsonline clustering020201 artificial intelligence & image processingpartitioning around medoidsAlgorithm
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On-line Construction of Two-Dimensional Suffix Trees

1999

AbstractWe say that a data structure is builton-lineif, at any instant, we have the data structure corresponding to the input we have seen up to that instant. For instance, consider the suffix tree of a stringx[1,n]. An algorithm building iton-lineis such that, when we have read the firstisymbols ofx[1,n], we have the suffix tree forx[1,i]. We present a new technique, which we refer to asimplicit updates, based on which we obtain: (a) an algorithm for theon-lineconstruction of the Lsuffix tree of ann×nmatrixA—this data structure is the two-dimensional analog of the suffix tree of a string; (b) simple algorithms implementing primitive operations forLZ1-typeon-line losslessimage compression m…

Statistics and ProbabilityCompressed suffix arrayNumerical AnalysisControl and OptimizationAlgebra and Number TheoryTheoretical computer scienceApplied MathematicsGeneral MathematicsSuffix treeString (computer science)Generalized suffix treelaw.inventionLongest common substring problemTree (data structure)lawSuffixAlgorithmFM-indexMathematicsJournal of Complexity
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Parametric estimation of non-crossing quantile functions

2021

Quantile regression (QR) has gained popularity during the last decades, and is now considered a standard method by applied statisticians and practitioners in various fields. In this work, we applied QR to investigate climate change by analysing historical temperatures in the Arctic Circle. This approach proved very flexible and allowed to investigate the tails of the distribution, that correspond to extreme events. The presence of quantile crossing, however, prevented using the fitted model for prediction and extrapolation. In search of a possible solution, we first considered a different version of QR, in which the QR coefficients were described by parametric functions. This alleviated th…

Statistics and ProbabilityComputer scienceConstrained optimizationquantile crossingR packageQRcmPopularityconstrained optimizationQuantile regression coefficients modelling (QRCM)Quantile regressionWork (electrical)constrained optimization; parametric quantile functions; quantile crossing; Quantile regression coefficients modelling (QRCM); R packageQRcmParametric estimationEconometricsparametric quantile functionsStatistics Probability and UncertaintyQuantile
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