Search results for " method"

showing 10 items of 10455 documents

Energy minimization of single input orbit transfer by averaging and continuation

2006

AbstractThis article deals with the transfer between Keplerian coplanar orbits using low propulsion. We focus on the energy minimization problem and compute the averaged system, proving integrability and relating the corresponding trajectories to a three-dimensional Riemannian problem that is analyzed in details. The geodesics provide approximations of the extremals of the energy minimization problem and can be used in order to evaluate the optimal trajectories of the time optimal and the minimization of the consumption problems with continuation methods. In particular, minimizing trajectories for transfer towards the geostationary orbit can be approximated in suitable coordinates by straig…

[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC]0209 industrial biotechnologyMathematics(all)GeodesicGeneral MathematicsMoyennation02 engineering and technologyPropulsionEnergy minimization01 natural sciencesContinuationAveraging020901 industrial engineering & automation0101 mathematicsMinimisation de l'énergieComputingMilieux_MISCELLANEOUSMathematicsTransfert orbital à poussée faibleMéthodes de continuation010102 general mathematicsMathematical analysis[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Orbital transfer with low thrustEnergy minimizationContinuation methodsOrbit (dynamics)Geostationary orbit[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]MinificationFocus (optics)
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Regularization of chattering phenomena via bounded variation controls

2018

In control theory, the term chattering is used to refer to strong oscillations of controls, such as an infinite number of switchings over a compact interval of times. In this paper we focus on three typical occurences of chattering: the Fuller phenomenon, referring to situations where an optimal control switches an infinite number of times over a compact set; the Robbins phenomenon, concerning optimal control problems with state constraints, meaning that the optimal trajectory touches the boundary of the constraint set an infinite number of times over a compact time interval; the Zeno phenomenon, referring as well to an infinite number of switchings over a compact set, for hybrid optimal co…

[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC]0209 industrial biotechnologyState constraintsBoundary (topology)02 engineering and technologyInterval (mathematics)01 natural sciences020901 industrial engineering & automationShooting methodConvergence (routing)FOS: MathematicsApplied mathematicsHybrid problems0101 mathematicsElectrical and Electronic EngineeringMathematics - Optimization and ControlMathematicsTotal variation010102 general mathematics[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Optimal controlComputer Science ApplicationsControllabilityControl and Systems EngineeringOptimization and Control (math.OC)Chattering controlBounded variationTrajectory[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]Fuller phenomenon
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Convergence rate of a relaxed inertial proximal algorithm for convex minimization

2018

International audience; In a Hilbert space setting, the authors recently introduced a general class of relaxed inertial proximal algorithms that aim to solve monotone inclusions. In this paper, we specialize this study in the case of non-smooth convex minimization problems. We obtain convergence rates for values which have similarities with the results based on the Nesterov accelerated gradient method. The joint adjustment of inertia, relaxation and proximal terms plays a central role. In doing so, we highlight inertial proximal algorithms that converge for general monotone inclusions, and which, in the case of convex minimization, give fast convergence rates of values in the worst case.

[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC]Class (set theory)Control and OptimizationInertial frame of referenceLyapunov analysis0211 other engineering and technologies02 engineering and technologyManagement Science and Operations Research01 natural sciencessymbols.namesakenonsmooth convex minimizationrelaxationweak-convergence0101 mathematics[MATH]Mathematics [math]point algorithmMathematics021103 operations researchWeak convergence[QFIN]Quantitative Finance [q-fin]Applied MathematicsHilbert space[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]dynamicsmaximally monotone operatorsInertial proximal method010101 applied mathematicsMonotone polygonRate of convergenceConvex optimizationmaximal monotone-operatorssymbolsRelaxation (approximation)[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]subdifferential of convex functionsAlgorithm
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Discrete and differential homotopy in circular restricted three-body control

2010

The planar circular restricted three-body problem is considered. The control enters linearly in the equation of motion to model the thrust of the third body. The minimum time optimal control problem has two scalar parameters: The ratio of the primaries masses which embeds the two-body problem into the three-body one, and the upper bound on the control norm. Regular extremals of the maximum principle are computed by shooting thanks to continuations with respect to both parameters. Discrete and di erential homotopy are compared in connection with second order sucient conditions in optimal control. Homotopy with respect to control bound gives evidence of various topological structures of extr…

[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC]Homotopy lifting propertyHomotopy010102 general mathematicsMathematical analysis[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Optimal control01 natural sciencesUpper and lower boundsRegular homotopyn-connectedMaximum principle0103 physical sciences[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]0101 mathematics010303 astronomy & astrophysicsHomotopy analysis methodComputingMilieux_MISCELLANEOUSMathematics
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A combination of algebraic, geometric and numerical methods in the contrast problem by saturation in magnetic resonance imaging

2014

In this article, the contrast imaging problem by saturation in nuclear magnetic resonance is modeled as a Mayer problem in optimal control. The optimal solution can be found as an extremal solution of the Maximum Principle and analyzed with the recent advanced techniques of geometric optimal control. This leads to a numerical investigation based on shooting and continuation methods implemented in the HamPath software. The results are compared with a direct approach to the optimization problem and implemented within the Bocop toolbox. In complement lmi techniques are used to estimate a global optimum. It is completed with the analysis of the saturation problem of an ensemble of spin particle…

[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC]Moment optimization[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Direct methodContrast imaging in NMR[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]Geometric optimal controlShooting and continuation techniques
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Differential inclusions involving normal cones of nonregular sets in Hilbert spaces

2017

This thesis is dedicated to the study of differential inclusions involving normal cones of nonregular sets in Hilbert spaces. In particular, we are interested in the sweeping process and its variants. The sweeping process is a constrained differential inclusion involving normal cones which appears naturally in several applications such as elastoplasticity, electrical circuits, hysteresis, crowd motion, etc.This work is divided conceptually in three parts: Study of positively alpha-far sets, existence results for differential inclusions involving normal cones and characterizations of Lyapunov pairs for the sweeping process. In the first part (Chapter 2), we investigate the class of positivel…

[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC]cône normalMoreau-Yosida regularizationcono normalmétodo de tipo Galerkinfonction distanceGalerkin-like methodMSC: 34A60 49J52 34G25 49J53 34B10 93D30subdiferencial de Clarkeprocessus de rafleInclusión diferencialensembles positivement alpha-far'sweeping processfonctions de Lyapunovsous-différentiel de Clarkeprocesos de arrastrefunción distanciaLyapunov functionsconjuntos positivamente alpha-farFunciones de Lyapunovméthode de type Galerkinrégularisation de Moreau-YosidaDifferential inclusions[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Clarke subdifferentialregularización de Moreau-YosidaDistance functionInclusion différentielle[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]Normal conepositively alpha-far sets
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Functional ecology for evaluating and predicting the aptitude of permanent grassland to provide services

2013

National audience; Applying the principles of functional ecology helps better predict the services which can be provided by permanent grassland. Farming practices and environmental characteristics influence the functional composition of grassland. Functional plant types have a generic effect on forage services which can be assessed. 13 types of grassland composed of distinct functional types were defined based on 1,283 sample grassland fields located on 8 sites. This approach was validated by checking 8 Ellenberg indicator values (used to evaluate environmental characteristics and farming practices) against climatic data and descriptive data for farming practices. The interest of a function…

[ SDV ] Life Sciences [q-bio]flexibility of managementgrasspasture management practices[SDV]Life Sciences [q-bio]forage productionseasonal variationsBiodiversitygrassland typologynutritive valuenitrogenfertilisation[SHS]Humanities and Social Sciences[SDV] Life Sciences [q-bio]functional compositionenvironmental factorvegetation[SDE]Environmental Sciencesforage system[SDV.BV]Life Sciences [q-bio]/Vegetal Biologyutilization value of grasslandsservices provided by grasslandpermanent pastureestimation method
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Computerized delimitation of odorant areas in gas-chromatography-olfactometry by kernel density estimation: Data processing on French white wines

2017

International audience; GC-O using the detection frequency method gives a list of odor events (OEs) where each OE is described by a linear retention index (LRI) and by the aromatic descriptor given by a human assessor. The aim of the experimenter is to gather OEs in a total olfactogram on which he tries to delimit odorant areas (OAs), then to compute each detection frequency. This paper proposes a computerized mathematical method based on kernel density estimation that makes up the total olfactogram as continuous and differentiable function from the OEs LRI only. The corresponding curve looks like a chromatogram, the peaks of which are potential OAs. The limits of an OA are the LRI of the t…

[ SDV.AEN ] Life Sciences [q-bio]/Food and NutritionKernel density estimation01 natural sciencesolfactogramAnalytical ChemistrySet (abstract data type)0404 agricultural biotechnologyStatisticsRange (statistics)Kernel densitu estimationSpectroscopyMathematicsContingency tableProcess Chemistry and Technology010401 analytical chemistry04 agricultural and veterinary sciencesdetection frequency method040401 food science0104 chemical sciencesComputer Science ApplicationsMaxima and minimaGC olphactometryKernel (statistics)Benchmark (computing)Kovats retention indexParzen-Rosenblatt[SDV.AEN]Life Sciences [q-bio]/Food and NutritionSoftware
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Insights into genome plasticity of the wine-making bacterium Oenococcus oeni strain ATCC BAA-1163 by decryption of its whole genome.

2008

International audience; Studying genomes of O. oeni strains having opposite oenological aptitudes is important for understanding why this lactic acid bacterium involved in malolactic fermentation is so well adapted to wine. Here, the genome of a strain ATCC BAA-1163, is described and compared with the recently reported genome of the better wine-adapted strain PSU-1. The BAA-1163 genome (8X) was obtained by shotgun sequencing and Phrap assembling. Compact and 62% AT-rich, it consists of a circular 1,792,103-bp chromosome and a 3,948-bp plasmid. It was analysed through a CAAT-Box annotation platform and manually inspected. A total of 51 RNA genes were detected, including two rRNA operons (the…

[ SDV.BID.EVO ] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE][SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM][ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM][SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE][SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE][ SDV.MP.BAC ] Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM][SDV.MP.BAC] Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology[ SDV.BIBS ] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM][SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM][SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
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Methodology for assessment of measuring uncertainties of articulated arm coordinates measuring machine

2014

International audience; The Articulated Arm Coordinate Measuring Machines (AACMM) have gradually evolved and are increasingly used in mechanic industry. At present, measurement uncertainties relating to the use of these devices are not yet well-quantified. The work carried out consists on determining the measurement uncertainties of a mechanical part by an Articulated Arm Coordinate Measuring Machine. The studies aiming to develop a model of measurement uncertainties are based on the Monte Carlo method developed in Supplement 1 of the Guide to Expression of Uncertainty in Measurement [1] but also identifying and characterizing the main sources of uncertainty. A Multi-level Monte Carlo appro…

[ SPI.MECA.GEME ] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanical engineering [physics.class-ph]0209 industrial biotechnologyComputer scienceApplied MathematicsMonte Carlo methodWork (physics)Uncertainty[PHYS.MECA.GEME]Physics [physics]/Mechanics [physics]/Mechanical engineering [physics.class-ph]Monte Carlo SimulationControl engineering02 engineering and technologyCoordinate-measuring machineArticulated Arm Coordinate Measuring Machine01 natural sciencesExpression (mathematics)[SPI.MECA.GEME]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanical engineering [physics.class-ph]010309 optics020901 industrial engineering & automation0103 physical sciences[ PHYS.MECA.GEME ] Physics [physics]/Mechanics [physics]/Mechanical engineering [physics.class-ph]CalibrationMeasurement uncertaintyPoint (geometry)InstrumentationEngineering (miscellaneous)
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