Search results for "ExPEC"

showing 10 items of 585 documents

A critical plane approach based on energy concepts: application to biaxial random tension-compression high-cycle fatigue regime

1999

Abstract In this paper the energy parameter, defined for random loadings, is analysed. Under uniaxial loading this parameter distinguishes between the strain energy density for tension (positive) and the strain energy density for compression (negative). As a consequence, if there is no mean component in the random loading, we obtain a random history of strain (elastic and plastic) energy density with zero expected value. Under multiaxial loadings the normal strain energy density in the critical plane (i.e. the plane of the maximum damage) is understood as the energy parameter. The history of strain energy density is schematized with use of the rain-flow algorithm. Fatigue damage is accumula…

Materials sciencebusiness.industryPlane (geometry)Tension (physics)Mechanical EngineeringBiaxial tensile testStrain energy density functionStructural engineeringMechanicsExpected valueCompression (physics)Industrial and Manufacturing EngineeringStrain energyMechanics of MaterialsModeling and SimulationGeneral Materials SciencebusinessVibration fatigueInternational Journal of Fatigue
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Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection

2016

Graphical abstractDisplay Omitted HighlightsWe consider a constrained three-objective optimization portfolio selection problem.We solve the problem by means of evolutionary multi-objective optimization.New mutation, crossover and reparation operators are designed for this problem.They are tested in several algorithms for a data set from the Spanish stock market.Results for two performance metrics reveal the effectiveness of the new operators. In this paper, we consider a recently proposed model for portfolio selection, called Mean-Downside Risk-Skewness (MDRS) model. This modelling approach takes into account both the multidimensional nature of the portfolio selection problem and the requir…

Mathematical optimization021103 operations researchOptimization problemCrossover0211 other engineering and technologiesEvolutionary algorithm02 engineering and technologyFuzzy logicMulti-objective optimization0202 electrical engineering electronic engineering information engineeringExpected returnPortfolio020201 artificial intelligence & image processingAlgorithmSoftwarePossibility theoryMathematicsApplied Soft Computing
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Bayesian estimation of edge orientations in junctions

1999

Abstract Junctions, defined as those points of an image where two or more edges meet, play a significant role in many computer vision applications. Junction detection is a widely treated problem, and some detectors can provide even the directions of the edges that meet in a junction. The main objective of this paper is the precise estimation of such directions. It is supposed that the junction point has been previously found by some detector. Also, it is assumed that samples, possibly noisy, of orientations of the edges found in a circular window surrounding the point are available. A mixture of von Mises distributions is assumed for these data, and then a Bayesian methodology is applied to…

Mathematical optimizationBayes estimatorBayesian probabilityDetectorPosterior probabilityMarkov chain Monte CarloExpected valueReal imagesymbols.namesakeArtificial IntelligenceSignal ProcessingsymbolsPoint (geometry)Computer Vision and Pattern RecognitionAlgorithmSoftwareMathematicsPattern Recognition Letters
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Continuous-time portfolio optimization under terminal wealth constraints

1995

Typically portfolio analysis is based on the expected utility or the mean-variance approach. Although the expected utility approach is the more general one, practitioners still appreciate the mean-variance approach. We give a common framework including both types of selection criteria as special cases by considering portfolio problems with terminal wealth constraints. Moreover, we propose a solution method for such constrained problems.

Mathematical optimizationComputer scienceGeneral MathematicsConstrained optimizationManagement Science and Operations ResearchReplicating portfolioPortfolioPost-modern portfolio theoryProject portfolio managementPortfolio optimizationMathematical economicsSoftwareExpected utility hypothesisModern portfolio theoryZOR Zeitschrift f�r Operations Research Methods and Models of Operations Research
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Optimal control of option portfolios and applications

1999

We present an expected utility maximisation framework for optimally controlling a portfolio of options. By combining the replication approach to option pricing with ideas of the martingale approach to (stock) portfolio optimisation we arrive at an explicit solution of the option portfolio problem. Its characteristics are illustrated by some specific examples. As an application, we calculate an optimal option and consumption strategy for an investor who is obliged to hold a stock position until the time horizon.

Mathematical optimizationComputer scienceMathematics::Optimization and ControlTime horizonManagement Science and Operations ResearchOptimal controlMartingale (betting system)Computer Science::Computational Engineering Finance and ScienceValuation of optionsBusiness Management and Accounting (miscellaneous)PortfolioPosition (finance)Expected utility hypothesisStock (geology)OR Spectrum
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Modelling agricultural risk in a large scale positive mathematical programming model

2020

International audience; Mathematical programming has been extensively used to account for risk in farmers' decision making. The recent development of the positive mathematical programming (PMP) has renewed the need to incorporate risk in a more robust and flexible way. Most of the existing PMP-risk models have been tested at farm-type level and for a very limited sample of farms. This paper presents and tests a novel methodology for modelling risk at individual farm level in a large scale model, called individual farm model for common agricultural policy analysis (IFM-CAP). Results show a clear trade-off between including and excluding the risk specification. Albeit both alternatives provid…

Mathematical optimizationEconomics and EconometricsScale (ratio)Computer scienceComputationprogrammation mathématique positive020209 energyexpected utilitySample (statistics)highest posterior density02 engineering and technologypolitique agricole communerisk and uncertainty0202 electrical engineering electronic engineering information engineeringEuropean common agricultural policyExpected utility hypothesisagricultureEstimationrisque et incertitude2. Zero hungerbusiness.industry020208 electrical & electronic engineering[SHS.ECO]Humanities and Social Sciences/Economics and Finance16. Peace & justicemodèle de fermePMPComputer Science ApplicationsAgriculturebusinessCommon Agricultural PolicyScale modelpositive mathematical programmingInternational Journal of Computational Economics and Econometrics
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Conflict resolution in the multi-stakeholder stepped spillway design under uncertainty by machine learning techniques

2021

Abstract The optimal spillway design is of great significance since these structures can reduce erosion downstream of the dams. This study proposes a risk-based optimization framework for a stepped spillway to achieve an economical design scenario with the minimum loss in hydraulic performance. Accordingly, the stepped spillway was simulated in the FLOW-3D® model, and the validated model was repeatedly performed for various geometric states. The results were used to form a Multilayer Perceptron artificial neural network (MLP-ANN) surrogate model. Then, a risk-based optimization model was formed by coupling the MLP-ANN and NSGA-II. The concept of conditional value at risk (CVaR) was utilized…

Mathematical optimizationExpected shortfallSpillwaySurrogate modelArtificial neural networkComputer scienceCVARMultilayer perceptronConflict resolutionStepped spillwayVDP::Technology: 500::Information and communication technology: 550SoftwareApplied Soft Computing
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On properties of the iterative maximum likelihood reconstruction method

1989

In this paper, we continue our investigations6 on the iterative maximum likelihood reconstruction method applied to a special class of integral equations of the first kind, where one of the essential assumptions is the positivity of the kernel and the given right-hand side. Equations of this type often occur in connection with the determination of density functions from measured data. There are certain relations between the directed Kullback–Leibler divergence and the iterative maximum likelihood reconstruction method some of which were already observed by other authors. Using these relations, further properties of the iterative scheme are shown and, in particular, a new short and elementar…

Mathematical optimizationIterative proportional fittingIterative methodGeneral MathematicsKernel (statistics)Expectation–maximization algorithmGeneral EngineeringApplied mathematicsIterative reconstructionDivergence (statistics)Integral equationLocal convergenceMathematicsMathematical Methods in the Applied Sciences
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Self-stabilizing Balls & Bins in Batches

2016

A fundamental problem in distributed computing is the distribution of requests to a set of uniform servers without a centralized controller. Classically, such problems are modelled as static balls into bins processes, where m balls (tasks) are to be distributed to n bins (servers). In a seminal work, [Azar et al.; JoC'99] proposed the sequential strategy Greedy[d] for n = m. When thrown, a ball queries the load of d random bins and is allocated to a least loaded of these. [Azar et al.; JoC'99] showed that d=2 yields an exponential improvement compared to d=1. [Berenbrink et al.; JoC'06] extended this to m ⇒ n, showing that the maximal load difference is independent of m for d=2 (in contrast…

Mathematical optimizationMarkov chainSelf-stabilization0102 computer and information sciencesNew variantExpected value01 natural sciencesBinExponential functionCombinatorics010104 statistics & probability010201 computation theory & mathematicsTheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITYServerBall (bearing)0101 mathematicsMathematicsProceedings of the 2016 ACM Symposium on Principles of Distributed Computing
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A Conditional Value–at–Risk Model for Insurance Products with Guarantee

2009

We propose a model to select the optimal portfolio which underlies insurance policies with a guarantee. The objective function is defined in order to minimise the conditional value at-risk (CVaR) of the distribution of the losses with respect to a target return. We add operational and regulatory constraints to make the model as flexible as possible when used for real applications. We show that the integration of the asset and liability side yields superior performances with respect to naive fixed-mix portfolios and asset based strategies. We validate the model on out-of-sample scenarios and provide insights on policy design.

Mathematical optimizationPortfolio selection.Actuarial scienceComputer scienceCVARAsset-liability managementAsset-liability management; Conditional value-at-risk; CVaR; Policies with a minimum guarantee; Portfolio selection.Management Science and Operations ResearchPolicies with a minimum guaranteeExpected shortfallInsurance policyReplicating portfolioPortfolioCapital asset pricing modelAsset (economics)Statistics Probability and UncertaintyBusiness and International ManagementPortfolio optimizationCVaRConditional value-at-risk
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