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Maria Daneva

A Conceptual Sequential Linear Programming Algorithm with Multidimensional Search

Within the field of nonlinear programming, a frequently employed solution principle is to alternate between the solution of an approximate problem and a line search with respect to a merit function, that measures the degree of non-optimality of any tentative solution. The Sequential Linear Programming (SLP) approach is a method that exploits this principle. We use the generic column generation scheme to develop a novel SLP type method for constrained nonlinear optimization. It is based on linear approximation of both the primal and the dual spaces, which yields a method which in the primal space combines column and constraints generation. In the presented algorithm we do not need to find rules to control the move limits and we can also skip the merit function, but steel get a convergence to a point that satisfy a Karush-Kuhn-Tucker conditions in the non-convex case. I am going to visualize the proposed algorithm with a simple example and present some computational experiments on frequently used nonlinear optimization problems.

Sidansvarig: karin.johansson@liu.se
Senast uppdaterad: 2014-10-14