Search results for "Discretization"

showing 7 items of 237 documents

Interface Detection Using a Quenched-Noise Version of the Edwards-Wilkinson Equation

2015

We report here a multipurpose dynamic-interface-based segmentation tool, suitable for segmenting planar, cylindrical, and spherical surfaces in 3D. The method is fast enough to be used conveniently even for large images. Its implementation is straightforward and can be easily realized in many environments. Its memory consumption is low, and the set of parameters is small and easy to understand. The method is based on the Edwards-Wilkinson equation, which is traditionally used to model the equilibrium fluctuations of a propagating interface under the influence of temporally and spatially varying noise. We report here an adaptation of this equation into multidimensional image segmentation, an…

ta113Image segmentationta114DiscretizationInterface (Java)Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONobject detectionimage edge detectionImage segmentationComputer Graphics and Computer-Aided DesignGrayscaleGray-scaleObject detectionSurface topographyNoiseMathematical modelThree-dimensional displaysSegmentationTomography3D image processingNoiseSurface morphologyAlgorithmSoftwareIEEE Transactions on Image Processing
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Reduced Order Models for Pricing American Options under Stochastic Volatility and Jump-diffusion Models

2016

American options can be priced by solving linear complementary problems (LCPs) with parabolic partial(-integro) differential operators under stochastic volatility and jump-diffusion models like Heston, Merton, and Bates models. These operators are discretized using finite difference methods leading to a so-called full order model (FOM). Here reduced order models (ROMs) are derived employing proper orthogonal decomposition (POD) and non negative matrix factorization (NNMF) in order to make pricing much faster within a given model parameter variation range. The numerical experiments demonstrate orders of magnitude faster pricing with ROMs. peerReviewed

ta113Mathematical optimizationStochastic volatilityDiscretizationComputer scienceJump diffusionFinite difference method010103 numerical & computational mathematics01 natural sciencesNon-negative matrix factorization010101 applied mathematicsValuation of optionslinear complementary problemRange (statistics)General Earth and Planetary SciencesApplied mathematicsreduced order modelFinite difference methods for option pricing0101 mathematicsAmerican optionoption pricingGeneral Environmental ScienceProcedia Computer Science
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Iterative Methods for Pricing American Options under the Bates Model

2013

We consider the numerical pricing of American options under the Bates model which adds log-normally distributed jumps for the asset value to the Heston stochastic volatility model. A linear complementarity problem (LCP) is formulated where partial derivatives are discretized using finite differences and the integral resulting from the jumps is evaluated using simple quadrature. A rapidly converging fixed point iteration is described for the LCP, where each iterate requires the solution of an LCP. These are easily solved using a projected algebraic multigrid (PAMG) method. The numerical experiments demonstrate the efficiency of the proposed approach. Furthermore, they show that the PAMG meth…

ta113Mathematical optimizationStochastic volatilityDiscretizationIterative methodComputer scienceFinite difference methodLinear complementarity problemIterative methodQuadrature (mathematics)Multigrid methodFixed-point iterationBates modelLinear complementarity problemGeneral Earth and Planetary SciencesPartial derivativeAmerican optionGeneral Environmental ScienceProcedia Computer Science
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A New Augmented Lagrangian Approach for $L^1$-mean Curvature Image Denoising

2015

Variational methods are commonly used to solve noise removal problems. In this paper, we present an augmented Lagrangian-based approach that uses a discrete form of the L1-norm of the mean curvature of the graph of the image as a regularizer, discretization being achieved via a finite element method. When a particular alternating direction method of multipliers is applied to the solution of the resulting saddle-point problem, this solution reduces to an iterative sequential solution of four subproblems. These subproblems are solved using Newton’s method, the conjugate gradient method, and a partial solution variant of the cyclic reduction method. The approach considered here differs from ex…

ta113Mean curvatureDiscretizationimage denoisingAugmented Lagrangian methodApplied MathematicsGeneral Mathematicsmean curvaturekuvankäsittelyTopologyFinite element methodimage processingsymbols.namesakeLagrangian relaxationLagrange multiplierConjugate gradient methodsymbolsApplied mathematicsaugmented Lagrangian methodalternating direction methods of multipliersvariational modelMathematicsCyclic reductionSIAM Journal on Imaging Sciences
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IMEX schemes for pricing options under jump–diffusion models

2014

We propose families of IMEX time discretization schemes for the partial integro-differential equation derived for the pricing of options under a jump-diffusion process. The schemes include the families of IMEX-midpoint, IMEX-CNAB and IMEX-BDF2 schemes. Each family is defined by a convex combination parameter [email protected]?[0,1], which divides the zeroth-order term due to the jumps between the implicit and explicit parts in the time discretization. These IMEX schemes lead to tridiagonal systems, which can be solved extremely efficiently. The schemes are studied through Fourier stability analysis and numerical experiments. It is found that, under suitable assumptions and time step restric…

ta113Numerical AnalysisMathematical optimizationTridiagonal matrixDiscretizationApplied MathematicsJump diffusionStability (probability)Term (time)Computational MathematicsValuation of optionsConvex combinationLinear multistep methodMathematicsApplied Numerical Mathematics
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Efficient Time Integration of Maxwell's Equations with Generalized Finite Differences

2015

We consider the computationally efficient time integration of Maxwell’s equations using discrete exterior calculus (DEC) as the computational framework. With the theory of DEC, we associate the degrees of freedom of the electric and magnetic fields with primal and dual mesh structures, respectively. We concentrate on mesh constructions that imitate the geometry of the close packing in crystal lattices that is typical of elemental metals and intermetallic compounds. This class of computational grids has not been used previously in electromagnetics. For the simulation of wave propagation driven by time-harmonic source terms, we provide an optimized Hodge operator and a novel time discretizati…

ta113crystal structureElectromagneticsDiscretizationApplied Mathematicsta111Mathematical analysisFinite differenceFinite-difference time-domain methodDegrees of freedom (statistics)harmonic Hodge operatordiscrete exterior calculusmesh generationComputational Mathematicssymbols.namesakeDiscrete exterior calculusMaxwell's equationsMaxwell's equationsMesh generationnonuniform time discretizationsymbolsMathematicsSIAM Journal on Scientific Computing
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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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