Search results for "Computational efficiency"

showing 3 items of 3 documents

Avoiding strange attractors in efficient parametric families of iterative methods for solving nonlinear problems

2019

[EN] Searching zeros of nonlinear functions often employs iterative procedures. In this paper, we construct several families of iterative methods with memory from one without memory, that is, we have increased the order of convergence without adding new functional evaluations. The main aim of this manuscript yields in the advantage that the use of real multidimensional dynamics gives us to decide among the different classes designed and, afterwards, to select its most stable members. Moreover, we have found some elements of the family whose behavior includes strange attractors of different kinds that must be avoided in practice. In this sense, Feigenbaum diagrams have resulted an extremely …

Feigenbaum diagramsNumerical AnalysisMathematical optimizationRelation (database)Iterative methodApplied MathematicsNonlinear problems010103 numerical & computational mathematicsConstruct (python library)01 natural sciencesComputational efficiency010101 applied mathematicsComputational MathematicsNonlinear systemRate of convergenceAttractorIterative methods with and without memoryNumerical tests0101 mathematicsMATEMATICA APLICADAQualitative analysisMathematicsParametric statisticsApplied Numerical Mathematics
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Approximation method for computationally expensive nonconvex multiobjective optimization problems

2012

Pareto-tehokkuusPareto optimalitycomputational efficiencyPareto front approximationpäätöksentekodecision makerpsychological convergencemonitavoiteoptimointilaskennallinen vaativuussurrogate functioninteractive decision makingmenetelmätPareto-optimointioptimointilaskennalliset menetelmätmultiobjective optimizationPareto dominancyapproksimointicomputational cost
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Midground Object Detection in Real World Video Scenes,

2007

Traditional video scene analysis depends on accurate background modeling to identify salient foreground objects. However, in many important surveillance applications, saliency is defined by the appearance of a new non-ephemeral object that is between the foreground and background. This midground realm is defined by a temporal window following the object's appearance; but it also depends on adaptive background modeling to allow detection with scene variations (e.g., occlusion, small illumination changes). The human visual system is ill-suited for midground detection. For example, when surveying a busy airline terminal, it is difficult (but important) to detect an unattended bag which appears…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionibusiness.industryComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScene statisticsObject (computer science)Object detectionObject-class detectionComputational efficiencyComputer networksSalientVideo trackingHuman visual system modelComputer visionViola–Jones object detection frameworkArtificial intelligencebusiness
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