Other methods for summing based on the SAT approach have been described [ 17], which are possibly easier to solve by the SAT solver so that a future version of SATlotyper will be further optimised.
However, for purposes of comparison with the other two risk-adjusters, we created a summary score for Elixhauser method by summing up the total number of Elixhauser comorbidities.
Li et al. [10] presented a proximal alternating linearization method for the sum of two nonconvex functions based on the assumption that the proximal point of the two functions at a given point can easily be calculated.
In particular, Kiwiel [9] proposed an alternating linearization bundle method for the sum of two convex functions and one of them is "simple" (i.e., minimizing this function plus a separable convex quadratic function is "easy").
The main advantage of this method is that the assumptions made for summed scores are no longer necessary while, in contrast with the prediction based on individual items, a latent variable is still assumed.
For both evaluation methods, summing flow values of single vessels, as well as integrating arterial flow in a large area, the measured values are distributed closely around the lines computed from the total flow values averaged over all depths.
Combining this FFT based method for the reciprocal sum with the direct evaluation of the real space sum, a spectrally accurate algorithm with a total complexity of O(NlogN) is obtained.
So, since in our case we are dealing with nonclassical light, we should capitalize on some different method for estimating the sum (3).
We construct an approximation of the free space Green's function for the Helmholtz equation that splits the application of this operator between the spatial and the Fourier domains, as in Ewald's method for evaluating lattice sums.
In this paper, we propose an alternating linearization bundle method for minimizing the sum of a nonconvex function and a convex function, both of which are not necessarily differentiable.
