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.
 1 Review of variational inequalities in maximization case.
 2 Penalized problem for mean reverting equation.
 3 Impossibility of backward induction.
 4 Stochastic optimization over wavelet basis.
 VI. Scalar product in N-dimensions.
 VII. Wavelet transform of payoff function in N-dimensions.
 VIII. Solving N-dimensional PDEs.

## Impossibility of backward induction. his section documents generic and unsuccessful attempt to use backward induction, introduced in the sections ( Backward induction ) and ( Stochastic optimal control ) in combination with finite element technique and a wavelet basis.

We are considering an equation of the problem ( Penalized mean reverting problem ): Whatever discretization of the penalty term we choose to consider, we would aim to correct into the area after few, preferably one, time step. Why not do it directly? Indeed, we start from a final payoff that satisfies . Thus, penalty term would be zero. We make one step of penalty-free evolution, apply some kind of a procedure that corrects the solution to and then do the same again.

We state with better precision what we intend to accomplish.

We have a solution given by a linear combination of a wavelet basis functions : We have another collection of non-negative valued functions with the same approximating power: but without well conditioned Gram matrix and We introduce the functionals and notations The are linear functionals and their action is easily computable: We would like to find a procedure for modification with two requirements for all : Let  Altogether there are requirements and variables . The requirement must be satisfied strictly because this is the area of primary interest: area of no exercise. We know that this is possible because the satisfies . For each , the equality is a hyperplane and taken for all is an intersection of hyperplanes. Since there are more requirements then variables, existence of intersection is likely to be numerically unstable. Therefore, we switch to the following problem where is a parameter dictating strictness of : and are the scale-dependent factors we intend to manipulate for better stability. The penalty term is introduced for regularization.

The function has the form where is an matrix and We proceed to calculate the minimum. Let be any non-zero vector and is a small positive real number, then  where the last equality holds at the minimum for any . Therefore, is recovered by solving The limit is called "Moore-Penrose pseudoinverse". Calculation of is a well developed area. We conclude The numerical experiment is implemented in the script soBackInd.py located in the directory OTSProjects/python/wavelets2. The procedure is unstable. The parameters and create wild difference in results.

We offer the following intuition about sources of instability. We need to have non-negative valued functions with the same approximating power: The only way to accomplish this within wavelet framework and in a situation of adaptive multiscaled basis is to take -based scaling functions as the collection . Those are the only non-negative functions in the framework. Functions only have the same approximating power if several of them are used with consecutive indexes. Multiscaling feature means that we take -ranges of for several . But then the entire collection would be linearly dependent, because by construction of , and this is one source of the instability.

Using single scale collection of almost removes instability but lacks precision. If we use fine scale collection of then we defeat the purpose of using wavelets: we get a large poorly conditioned matrix .

Another source of instability is the operation . We already noted that have good approximating properties when taken together. Here, we take a single scalar product. This is as good (or as bad) as taking scalar product with a hut function. If we revert to some simple family of , such as hut functions, then we get a large .

Finally, we cannot replace a single scalar product operation with a projection on a range of because we would be unable to take the maximum component-wise.

Naturally, in one dimensional situation, one can simply take directly. Indeed, both and are piecewise polynomial functions. There are two problems with this approach:

1. We need to recombine decompositions into piecewise polynomial functions, take maximum and decompose again.

2. Such procedure, although already expensive, has no effective extension in multidimensional case.

In the following sections we present a procedure that does not involve taking maximum and extends smoothly into multiple dimensions.