O. Epelly, J. Gondzio and J.-P. Vial
Solving large-scale optimization economic models such as Markal-Macro models proves to be difficult or even out of reach for state-of-the-art solvers. We propose an optimizer which takes advantage of their possible special structure: a large dynamic linear block on one side, a small nonlinear convex block on the other one. This framework favors the use of interior point methods which are efficient for large-scale linear programs and which can handle convex programs. NLPHOPDM is an implementation of an interior point method built upon the HOPDM code for linear and convex quadratic optimization The method combines ideas of a globally convergent algorithm and the extension of multiple centrality correctors technique to nonlinear convex programming. It is designed for being hooked to modeling languages such as GAMS and AMPL. We present in this paper preliminary results relative to our research code NLPHOPDM and to commercial nonlinear solvers. Our approach achieves a significant computational speed-up. This is performed via the use of a library which computes exact second derivatives.
Key words: Interior Point Method, Economic Model, Smooth Convex Optimization.