Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
On the use of boundary conditions for variational formulations arising in financial mathematics
Applied Mathematics and Computation
Rapid evaluation of radial basis functions
Journal of Computational and Applied Mathematics
Eigenvalues for equivariant matrices
Journal of Computational and Applied Mathematics - Special issue on computational and mathematical methods in science and engineering (CMMSE-2004)
Tools for Computational Finance (Universitext)
Tools for Computational Finance (Universitext)
Pricing European multi-asset options using a space-time adaptive FD-method
Computing and Visualization in Science
Fast Fourier Transforms on Finite Non-Abelian Groups
IEEE Transactions on Computers
Designing for geometrical symmetry exploitation
Scientific Programming - Parallel/High-Performance Object-Oriented Scientific Computing (POOSC '05), Glasgow, UK, 25 July 2005
Improved radial basis function methods for multi-dimensional option pricing
Journal of Computational and Applied Mathematics
Computers & Mathematics with Applications
Stable computation of multiquadric interpolants for all values of the shape parameter
Computers & Mathematics with Applications
Preconditioning for radial basis functions with domain decomposition methods
Mathematical and Computer Modelling: An International Journal
Improved radial basis function methods for multi-dimensional option pricing
Journal of Computational and Applied Mathematics
Applied Numerical Mathematics
Computers & Mathematics with Applications
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We show that the generalized Fourier transform can be used for reducing the computational cost and memory requirements of radial basis function methods for multi-dimensional option pricing. We derive a general algorithm, including a transformation of the Black-Scholes equation into the heat equation, that can be used in any number of dimensions. Numerical experiments in two and three dimensions show that the gain is substantial even for small problem sizes. Furthermore, the gain increases with the number of dimensions.