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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.
V. Stochastic optimization in one dimension.
VI. Scalar product in N-dimensions.
1. Two-sided area of integration, positive a.
2. Two-sided area of integration, negative a.
3. Indexing integration domains.
4. Summary. Calculation of scalar product in N dimensions.
5. Indexing integration domains II.
6. Scalar product in N-dimensions. Test case 1.
7. Scalar product in N-dimensions. Test case 2.
8. Scalar product in N-dimensions. Test case 3.
9. Implementation of scalar product in N-dimensions.
VII. Wavelet transform of payoff function in N-dimensions.
VIII. Solving N-dimensional PDEs.
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Indexing integration domains II.


t remains to describe a recipe for construction of the maps $T$ , $T^{0}$ , $T^{+}$ .

The map $T^{n-1}$ indexes a matrix. We choose the "row-by-row" indexing. If MATH then MATH Therefore, if MATH then MATH and MATH

With $T^{n-1}$ so defined we can calculate the arrays MATH in parallel.

Therefore, to identify the mappings $T^{0}$ and $T^{+}$ it suffices to map domains of these mappings into the domain of $T^{n}$ . For each $t$ from domain of $T^{n-1}$ we perform on a separate thread the following calculation: MATH MATH where the last set is the set of numbers in domain of $T^{n}$ . The total size of calculated data is known apriori after the arrays MATH have been calculated. Hence, after we calculate each $t$ on a separate thread, we do not need to merge data because the collection can happen directly into correct place.

To be precise, let MATH and MATH is obtained via the procedure of the section ( Calculation of partial sums in parallel ). We allocate an array MATH For every MATH (thread index) we calculate MATH as described above. We allocate the result into MATH as follows. For $t=0$ the result is placed into MATH For $t=1$ the result is placed into MATH For any MATH the result is placed into MATH The result MATH is the indexing of the boundary subdomains and MATH is enumeration of the boundary subdomains.

The $T^{+}$ case is almost identical. We replace MATH with MATH Indeed, we are interested in the area MATH We arrive to the following procedure. MATH Allocate MATH and, for every MATH , place result of MATH into MATH . Then MATH is the indexing of the internal subdomains and MATH is enumeration of the internal subdomains.





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