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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.
A. Example Black equation parameters.
B. Reduction to system of linear algebraic equations for Black PDE.
C. Adaptive time step for Black PDE.
D. Localization.
E. Reduction to system of linear algebraic equations for q=1.
F. Preconditioner for Black equation in case q=1.
a. Analytical preconditioner derived from asymptotic decomposition in time.
b. Diagonal preconditioner.
c. Symmetrization and symmetric preconditioning.
d. Reduction to well conditioned form.
e. Analytical preconditioner derived from inversion of Black equation.
G. Summary for Black equation in case q=1.
H. Implementation of Black equation solution.
8. Solving one dimensional 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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Analytical preconditioner derived from inversion of Black equation.


his section documents a negative result.

We would like to construct a preconditioner via exact inversion of the Black PDE.

In the section ( Asymptotic expansion for Black equation ) we calculated solution for Black equation. Let MATH be the solution of the problem MATH for integrable function $g\left( x\right) $ then MATH We would like to put the expression into the form MATH for some kernel MATH . We introduce the notation MATH then MATH We make the change MATH MATH then MATH MATH

We introduce the projections of $g,u,\QTR{cal}{E}$ on some basis MATH and index set $K$ . We approximate MATH then we apply the operation MATH to the equation MATH and obtain MATH Thus MATH where the $u,h$ are columns MATH and $G,E$ are matrixes MATH

Note that MATH and MATH MATH It follows from MATH MATH or MATH We intend to use (some approximation of) $G^{-1}\QTR{cal}{E}$ as a preconditioner. We need some way to evaluate the integrals MATH perhaps with relaxed precision. The simplest road is to use crudification operator to simplify piecewise polynomial representations of the basis MATH , make square of the number of pieces below 256 and then employ Cuda.

The functions MATH are piecewise quadratic polynomials. Our next task is to calculate expressions for the integrals MATH We calculate the $z$ -integral: MATH We make the change MATH MATH We complete the square: MATH MATH MATH Thus MATH We make the change MATH MATH

We cannot proceed with explicit evaluation of the $x$ -integral from this point. Hence, we perform a linear approximation of the function MATH on the interval of interest MATH : MATH MATH MATH MATH Make the change MATH then MATH We complete the square: MATH and do linear approximation MATH

We put our results together. MATH MATH MATH MATH MATH

Summary

(Integration of fundamental solution for Black equation) We calculate approximation of the integral MATH according to the following procedure MATH MATH and MATH is calculated as follows MATH MATH MATH where MATH

Due to lack of absolute precision and the number of required numerical operations this approach does not offer improvement over combined application of recipes of the sections ( Diagonal preconditioner ) and ( Reduction to well conditioned form ). The author did not do a numerical experiment or verification of the above summary.





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