size of $13 \times 13$ pixels. This is because the number of possible pixel locations increases, resulting in an increase in the number of non-zero coefficients in the symbolic terms. With a $13 \times 13$ patch, the “hp" method is only slightly more expensive than our method. This is unsurprising as the techniques proposed by Holzer [@Holzer2015] reduces training time by reducing the dimensionality of the matrix to be inverted, which depends on the number of pixels used. Similar to Fig. \[fig:synthetic\_lp\](a), the accuracy of all methods improve with an increasing patch size. Increasing the maximum parameter variation results in a similar pattern to Fig. \[fig:synthetic\_lp\](b) in terms of timing for the same reasons above. The accuracy of all methods degrades as the amount of parameter variation increases.

Note that the “jd" RMSE plot overlaps with the RMSE plot of our approach, whereas the “symdct-25" RMSE plot overlaps with the “dct-25" RMSE plot. This is because the LP obtained by the “sym" method is **identical** to the LP of the “jd" method, and the same applies to the LPs obtained by the “symdct-25" and the “dct-25". The only difference is that our approach produces a much shorter learning time.

Finally, 