Search results for "Dynamic programming principle"

showing 8 items of 8 documents

Asymptotic Lipschitz regularity for tug-of-war games with varying probabilities

2018

We prove an asymptotic Lipschitz estimate for value functions of tug-of-war games with varying probabilities defined in $\Omega\subset \mathbb R^n$. The method of the proof is based on a game-theoretic idea to estimate the value of a related game defined in $\Omega\times \Omega$ via couplings.

osittaisdifferentiaaliyhtälötPure mathematicsComputer Science::Computer Science and Game TheoryTug of war010102 general mathematicslocal Lipschitz estimatesLipschitz continuity01 natural sciencesnormalized p(x)-laplaciandynamic programming principle010104 statistics & probabilityMathematics - Analysis of PDEsFOS: Mathematicspeliteoria91A05 91A15 91A50 35B65 35J60 35J92stochastic games0101 mathematicsValue (mathematics)AnalysisAnalysis of PDEs (math.AP)Mathematicsstokastiset prosessit
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Solutions of nonlinear PDEs in the sense of averages

2012

Abstract We characterize p-harmonic functions including p = 1 and p = ∞ by using mean value properties extending classical results of Privaloff from the linear case p = 2 to all pʼs. We describe a class of random tug-of-war games whose value functions approach p-harmonic functions as the step goes to zero for the full range 1 p ∞ .

Class (set theory)Mean value theoremMathematics(all)Dynamic programming principleGeneral MathematicsAsymptotic expansion01 natural sciences1-harmonicApplied mathematics0101 mathematicsMathematicsp-harmonicApplied Mathematics010102 general mathematicsMathematical analysista111Zero (complex analysis)Sense (electronics)010101 applied mathematicsNonlinear systemRange (mathematics)Two-player zero-sum gamesMean value theorem (divided differences)Viscosity solutionsAsymptotic expansionValue (mathematics)Stochastic gamesJournal de Mathématiques Pures et Appliquées
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Regularity for nonlinear stochastic games

2015

We establish regularity for functions satisfying a dynamic programming equation, which may arise for example from stochastic games or discretization schemes. Our results can also be utilized in obtaining regularity and existence results for the corresponding partial differential equations. peerReviewed

viscosity solutionsDiscretization01 natural sciencesMathematics - Analysis of PDEsBellman equationComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATIONFOS: MathematicsApplied mathematicstug-of-war0101 mathematicsMathematics - Optimization and ControlMathematical PhysicsMathematicsstokastiset prosessitPartial differential equationApplied Mathematics91A15 35J92 35B65 35J60 49N60010102 general mathematicsta111dynamic programming principletug-of-war with noise with space dependent probabilities010101 applied mathematicsNonlinear systemOptimization and Control (math.OC)p-LaplaceAnalysisAnalysis of PDEs (math.AP)
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Hölder regularity for stochastic processes with bounded and measurable increments

2022

We obtain an asymptotic Hölder estimate for expectations of a quite general class of discrete stochastic processes. Such expectations can also be described as solutions to a dynamic programming principle or as solutions to discretized PDEs. The result, which is also generalized to functions satisfying Pucci-type inequalities for discrete extremal operators, is a counterpart to the Krylov-Safonov regularity result in PDEs. However, the discrete step size $\varepsilon$ has some crucial effects compared to the PDE setting. The proof combines analytic and probabilistic arguments.

todennäköisyyslaskentamatematiikkaApplied Mathematicsp-harmoniousProbability (math.PR)tug-of-war gamesstochastic processdynamic programming principlelocal Hölder estimatesFOS: Mathematicsequations in nondivergence formp-Laplace35B65 35J15 60H30 60J10 91A50Mathematical PhysicsAnalysisAnalysis of PDEs (math.AP)stokastiset prosessit
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Local regularity estimates for general discrete dynamic programming equations

2022

We obtain an analytic proof for asymptotic H\"older estimate and Harnack's inequality for solutions to a discrete dynamic programming equation. The results also generalize to functions satisfying Pucci-type inequalities for discrete extremal operators. Thus the results cover a quite general class of equations.

local Hölder estimateosittaisdifferentiaaliyhtälötABP-estimateApplied MathematicsGeneral Mathematicsp-LaplacianMathematics::Analysis of PDEs35B65 35J15 35J92 91A50elliptic non-divergence form partial differential equation with bounded and measurable coefficientsdynamic programming principleMathematics - Analysis of PDEsHarnack's inequalitytug-of-war with noiseFOS: MathematicsPucci extremal operatorpeliteoriaepäyhtälötAnalysis of PDEs (math.AP)
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Asymptotic Hölder regularity for the ellipsoid process

2020

We obtain an asymptotic Hölder estimate for functions satisfying a dynamic programming principle arising from a so-called ellipsoid process. By the ellipsoid process we mean a generalization of the random walk where the next step in the process is taken inside a given space dependent ellipsoid. This stochastic process is related to elliptic equations in non-divergence form with bounded and measurable coefficients, and the regularity estimate is stable as the step size of the process converges to zero. The proof, which requires certain control on the distortion and the measure of the ellipsoids but not continuity assumption, is based on the coupling method.

equations in non-divergence formControl and OptimizationDynamic programming principleGeneralizationSpace (mathematics)01 natural sciencesMeasure (mathematics)local Hölder estimatespeliteoriastochastic games0101 mathematicsstokastiset prosessitMathematicsosittaisdifferentiaaliyhtälötStochastic process010102 general mathematicsMathematical analysisRandom walkEllipsoidcoupling of stochastic processes010101 applied mathematicsDistortion (mathematics)Computational Mathematicsellipsoid processControl and Systems EngineeringBounded functionESAIM: Control, Optimisation and Calculus of Variations
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Convergence of dynamic programming principles for the $p$-Laplacian

2018

We provide a unified strategy to show that solutions of dynamic programming principles associated to the $p$-Laplacian converge to the solution of the corresponding Dirichlet problem. Our approach includes all previously known cases for continuous and discrete dynamic programming principles, provides new results, and gives a convergence proof free of probability arguments.

equivalent notions of solutions01 natural sciencesMathematics - Analysis of PDEsnumerical methodsConvergence (routing)FOS: MathematicsApplied mathematicsgeneralized viscosity solutiondiscrete approximationsMathematics - Numerical Analysis0101 mathematicsGeometry and topologyDirichlet problemMathematicsviscosity solutionosittaisdifferentiaaliyhtälötDirichlet problemasymptotic mean value propertiesconvergencenumeeriset menetelmätApplied Mathematics010102 general mathematicsNumerical Analysis (math.NA)dynamic programming principle010101 applied mathematicsDynamic programmingp-Laplacianmonotone approximationsapproksimointiAnalysisAnalysis of PDEs (math.AP)
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Game-Theoretic Approach to Hölder Regularity for PDEs Involving Eigenvalues of the Hessian

2021

AbstractWe prove a local Hölder estimate for any exponent $0<\delta <\frac {1}{2}$ 0 < δ < 1 2 for solutions of the dynamic programming principle $$ \begin{array}{@{}rcl@{}} u^{\varepsilon} (x) = \sum\limits_{j=1}^{n} \alpha_{j} \underset{\dim(S)=j}{\inf} \underset{|v|=1}{\underset{v\in S}{\sup}} \frac{u^{\varepsilon} (x + \varepsilon v) + u^{\varepsilon} (x - \varepsilon v)}{2} \end{array} $$ u ε ( x ) = ∑ j = 1 n α j inf dim ( S ) = j sup v ∈ S | v | = 1 u ε ( x + ε v ) + u ε ( x − ε v ) 2 with α1,αn > 0 and α2,⋯ ,αn− 1 ≥ 0. The proof is based on a new coupling idea from game theory. As an application, we get the same regularity estimate for viscosity solutions of the PDE $…

viscosity solutionosittaisdifferentiaaliyhtälötMathematics::Functional AnalysisStatistics::Theory91A05 91A15 35D40 35B65Mathematics::Dynamical Systemsholder estimateMathematics::Analysis of PDEsmatemaattinen optimointifully nonlinear PDEsdynamic programming principleMathematics - Analysis of PDEsMathematics::ProbabilityFOS: Mathematicspeliteoriaeigenvalue of the HessianAnalysisAnalysis of PDEs (math.AP)estimointi
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