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I. Wavelet calculations.
II. Calculation of approximation spaces in one dimension.
III. Calculation of approximation spaces in one dimension II.
IV. One dimensional problems.
1. Decomposition of payoff function in one dimension. Adaptive multiscaled approximation.
2. Constructing wavelet basis with Dirichlet boundary conditions.
3. Accelerated calculation of Gram matrix.
4. Adapting wavelet basis to arbitrary interval.
5. Solving one dimensional elliptic PDEs.
6. Discontinuous Galerkin technique II.
7. Solving one dimensional Black PDE.
8. Solving one dimensional mean reverting equation.
A. Reduction to system of linear algebraic equations for mean reverting equation.
B. Evaluating matrix R.
C. Localization for mean reverting equation.
D. Implementation for mean reverting equation.
V. Stochastic optimization in one dimension.
VI. Scalar product in N-dimensions.
VII. Wavelet transform of payoff function in N-dimensions.
VIII. Solving N-dimensional PDEs.
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Evaluating matrix R.


he matrixes MATH and MATH off the section ( Reduction to system of linear algebraic equations for mean reverting equation ) may be evaluated by the standard means provided by the library of the section ( Manipulation of localized piecewise polynomial functions ). However, the matrix MATH requires a custom Cuda coding. We perform the task in this section.

We need code for evaluation of the integral MATH where $P\left( x\right) $ was introduced in the section ( Manipulation of localized piecewise polynomial functions ): MATH MATH

We calculate MATH MATH We have a problem if $x-h$ is zero. In addition, we defeat the purpose of having a localized representation to avoid subtracting large values. By doing so in present situation we compound the problem. Therefore, we revert back to regular representation for the function $P\left( x\right) $ : MATH We evaluate MATH MATH In such case we can replace expressions MATH with zero: MATH

The implementation may be found in the project PiecewisePoly, function Poly::logIntegral.





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