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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.
VII. Wavelet transform of payoff function in N-dimensions.
1. Separation of variables for payoff function.
VIII. Solving N-dimensional PDEs.
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Separation of variables for payoff function.


ypically, in financial applications, the final payoff function depends on a small subset of variables MATH : MATH We represent such situation as MATH Thus MATH where the function MATH is the projection of $1$ on MATH in one dimension.

Assuming that MATH we get MATH We denote MATH then MATH thus MATH Note that for every derivative MATH there is exactly one term in the product that has dependency on the variable $y_{p_{j}j}$ . We continue: MATH The $i$ -sum is a basis decomposition. Hence, there is always an $i$ in the sum such that $i=p_{j}$ . MATH





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