3.   Discretization
 Consider a finite dimensional  approximation to a function u : We want to find an approximation to the first derivative w.r.t. x Simple partial derivatives like this are discretized using finite differences. The main steps are inverse  wavelet transform along the coordinate direction with respect to which we want to differentiate application of the univarite finite difference scheme wavelet transform with respect to the particular coordinate direction The following figure is a graphical sketch of this scheme for the 2D case. The squares on the left hand side represent the wavelet space with a certain arrangement of the indices/wavelet coefficients. The coloured entries are the coefficients for the indices from T (the colour corresponds to the magnitude).  In each step just one line (marked by the red bars) of coefficients is read and inverse transformed. This yields the nodal values of an univariate partial function on a (non-uniform) grid. To these values the finite difference scheme is applied. Then, a wavelet transform yields the coefficients of the result. This repeats for all lines. Vertical lines would be read/written for derivatives with respect to the y-coordinate direction. An analysis of the resulting consistency error is given in [6] for  regular sparse grids and in [4] for general adaptive sparse grids. Another idea for the discretization of differential operators is collocation. The main adavantages of the finite difference technique over collocation are: there is no restriction on the smoothness of the underlying wavelets. This allows to use low order wavelets which have a small support and are, therefore, algorithmically cheaper than smoother high order wavelets. even with the same wavelets, the operator evaluation is much cheaper for the finite difference scheme than for the collocation method it is quite simple to incorporate special finite differnce stencils, like ENO/WENO, for differential operators which require a special treatment for, e.g, stability reasons