IMPACT OF PARTIAL SEPARABILITY ON LARGE-SCALE OPTIMIZATION

ELSO is an environment for the solution of large-scale optimization pr oblems. With ELSO the user is required to provide only code for the ev aluation of a partially separable function. ELSO exploits the partial separability structure of the function to compute the gradient efficie ntly using automatic differentiation. We demonstrate ELSO's efficiency by comparing the various options available in ELSO. Our conclusion is that the hybrid option in ELSO provides performance comparable to the hand-coded option, while having the significant advantage of not requ iring a hand-coded gradient or the sparsity pattern of the partially s eparable function. In our test problems, which have carefully coded gr adients, the computing time for the hybrid AD option is within a facto r of two of the hand-coded option.