RESUMO
A variation of the classical backpropagation algorithm for neural network training is proposed and convergence is established using the perturbation results of Mangasarian and Solodov. The algorithm is similar to the successive overrelaxation (SOR) algorithm for systems of linear equations and linear complementary problems in using the most recently computed values of the weights to update the values on the remaining arcs.
RESUMO
Images that are to be transmitted through a distorting imaging system may be deliberately altered to compensate for that distortion. The authors consider an incoherent diffraction-limited imaging system followed by an ideal high-contrast detector that prints binary images, and seek a binary input image (a mask) that generates a desired prescribed binary output image. They have developed a method for determining the optimal binary mask by formulating the problem as a mixed linear integer program (MLIP) and using the branch and bound method to solve it.