Search results for "Order condition"

showing 4 items of 4 documents

NUMERICAL ALGORITHMS

2013

For many systems of differential equations modeling problems in science and engineering, there are natural splittings of the right hand side into two parts, one non-stiff or mildly stiff, and the other one stiff. For such systems implicit-explicit (IMEX) integration combines an explicit scheme for the non-stiff part with an implicit scheme for the stiff part. In a recent series of papers two of the authors (Sandu and Zhang) have developed IMEX GLMs, a family of implicit-explicit schemes based on general linear methods. It has been shown that, due to their high stage order, IMEX GLMs require no additional coupling order conditions, and are not marred by order reduction. This work develops a …

General linear methodsMathematical optimizationIMEX methods; general linear methods; error analysis; order conditions; stability analysisIMEX methodsDifferential equationSCHEMESorder conditionsMathematics AppliedExtrapolationStability (learning theory)QUADRATIC STABILITYstability analysisPARABOLIC EQUATIONSSYSTEMSNORDSIECK METHODSFOS: MathematicsApplied mathematicsMathematics - Numerical AnalysisRUNGE-KUTTA METHODSMULTISTEP METHODSerror analysisMathematicsCONSTRUCTIONSeries (mathematics)Applied MathematicsNumerical analysisComputer Science - Numerical AnalysisStability analysisORDEROrder conditionsNumerical Analysis (math.NA)Computer Science::Numerical AnalysisRunge–Kutta methodsGeneral linear methodsError analysisORDINARY DIFFERENTIAL-EQUATIONSOrdinary differential equationgeneral linear methodsMathematics
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Geometric optimal control : homotopic methods and applications

2012

This work is about geometric optimal control applied to celestial and quantum mechanics. We first dealt with the minimum fuel consumption problem of transfering a satellite around the Earth. This brought to the creation of the code HamPath which permits first of all to solve optimal control problem for which the command law is smooth. It is based on the Pontryagin Maximum Principle (PMP) and on the notion of conjugate point. This program combines shooting method, differential homotopic methods and tools to compute second order optimality conditions. Then we are interested in quantum control. We study first a system which consists in two different particles of spin 1/2 having two different r…

[SPI.OTHER]Engineering Sciences [physics]/OtherMéthodes de tirHomotopie différentielle[ SPI.OTHER ] Engineering Sciences [physics]/OtherOrbital transferContrôle optimal géométrique[SPI.OTHER] Engineering Sciences [physics]/Other[ MATH.MATH-GM ] Mathematics [math]/General Mathematics [math.GM]Shooting methodsDifferential homotopyAutomatic differentiationContraste en RMNQuantum control[MATH.MATH-GM] Mathematics [math]/General Mathematics [math.GM]Geometric optimal controlConditions du deuxième ordreTransfert orbitalLieux conjugués et de coupureDifférenciation automatiqueSecond order conditions[MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM]Cut and conjugate lociContrast imaging in NMRContrôle quantique
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Establishing some order amongst exact approximations of MCMCs

2016

Exact approximations of Markov chain Monte Carlo (MCMC) algorithms are a general emerging class of sampling algorithms. One of the main ideas behind exact approximations consists of replacing intractable quantities required to run standard MCMC algorithms, such as the target probability density in a Metropolis-Hastings algorithm, with estimators. Perhaps surprisingly, such approximations lead to powerful algorithms which are exact in the sense that they are guaranteed to have correct limiting distributions. In this paper we discover a general framework which allows one to compare, or order, performance measures of two implementations of such algorithms. In particular, we establish an order …

Statistics and ProbabilityFOS: Computer and information sciences65C05Mathematical optimizationMonotonic function01 natural sciencesStatistics - ComputationPseudo-marginal algorithm010104 statistics & probabilitysymbols.namesake60J05martingale couplingalgoritmitFOS: MathematicsApplied mathematics60J220101 mathematicsComputation (stat.CO)Mathematics65C40 (Primary) 60J05 65C05 (Secondary)Martingale couplingMarkov chainmatematiikkapseudo-marginal algorithm010102 general mathematicsProbability (math.PR)EstimatorMarkov chain Monte Carloconvex orderDelta methodMarkov chain Monte CarloOrder conditionsymbolsStatistics Probability and UncertaintyAsymptotic variance60E15Martingale (probability theory)Convex orderMathematics - ProbabilityGibbs sampling
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Continuous optimal control sensitivity analysis with AD

2000

In order to apply a parametric method to a minimum time control problem in celestial mechanics, a sensitivity analysis is performed. The analysis is continuous in the sense that it is done in the infinite dimensional control setting. The resulting sufficient second order condition is evaluated by means of automatic differentiation, while the associated sensitivity derivative is computed by continuous reverse differentiation. The numerical results are given for several examples of orbit transfer, also illustrating the advantages of automatic differentiation over finite differences for the computation of gradients on the discretized problem.

[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC]0209 industrial biotechnology021103 operations researchDiscretizationAutomatic differentiation0211 other engineering and technologiesFinite difference[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]02 engineering and technologyOptimal control020901 industrial engineering & automationOrder conditionControl theoryRiccati equationSensitivity (control systems)[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]ComputingMilieux_MISCELLANEOUSMathematicsParametric statistics
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