In multicriteria optimization, several objective functions, conflicting with each other, have to be minimized simultaneously. We propose a new efficient method for approximating the solution set of a multiobjective programming problem, where the objective functions involved are arbitary convex functions and the set of feasible points is convex. The method is based on generating warm-start points for an efficient interior-point algorithm, while the approximation computed consists of a finite set of discrete points. Complexity results for the method proposed are derived. It turns out that the number of operations per point \emph{decreases} when the number of points generated for the approximation \emph{increases}.
Citation
Ergebnisberichte Angewandte Mathematik, Nr.~248. Fachbereich Mathematik, Universit