Mathematica: a system for doing mathematics by computer (2nd ed.)
Mathematica: a system for doing mathematics by computer (2nd ed.)
Control-theoretic techniques for stepsize selection in implicit Runge-Kutta methods
ACM Transactions on Mathematical Software (TOMS)
Control Strategies for the Iterative Solution of Nonlinear Equations in ODE Solvers
SIAM Journal on Scientific Computing
RKC: an explicit solver for parabolic PDEs
Journal of Computational and Applied Mathematics
Fourth Order Chebyshev Methods with Recurrence Relation
SIAM Journal on Scientific Computing
Quadratic Convergence for Valuing American Options Using a Penalty Method
SIAM Journal on Scientific Computing
An Implicit-Explicit Runge--Kutta--Chebyshev Scheme for Diffusion-Reaction Equations
SIAM Journal on Scientific Computing
Adaptive stepsize based on control theory for stochastic differential equations
Journal of Computational and Applied Mathematics
RKC time-stepping for advection-diffusion-reaction problems
Journal of Computational Physics
Runge-Kutta-Chebyshev projection method
Journal of Computational Physics
S-ROCK: Chebyshev Methods for Stiff Stochastic Differential Equations
SIAM Journal on Scientific Computing
Penalty methods for the numerical solution of American multi-asset option problems
Journal of Computational and Applied Mathematics
Smoothing schemes for reaction-diffusion systems with nonsmooth data
Journal of Computational and Applied Mathematics
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American derivatives have become very popular instruments in financial markets. However, they are more complicated to price than European options since at each time level we have to determine not only the option value but also whether or not it should be exercised. Several procedures have been proposed to dissolve these difficulties, but they usually involve the solution of nonlinear partial differential equations (PDEs). In the case of multi-dimensional problems, solving these equations is a very challenging task. In this paper we propose Stabilized Explicit Runge-Kutta (SERK) methods to solve this kind of problems. They can easily be applied to many different classes of problems with large dimensions and they have low memory demand. Since these methods are explicit, they do not require algebra routines to solve large nonlinear systems associated to ODEs (as, for example, LAPACK and BLAS packages or multigrid or iterative methods applied together with Newton-type algorithms) and are especially well-suited for the method of lines (MOL) discretizations of parabolic PDEs.