In this paper we derive a lower bound on the average complexity of the Simplex-Method as a solution-process for linear programs (LP) of the type: We assume these problems to be randomly generated according to the Rotation-Symmetry-Model: *Let a 1 ,…,a m , v be distributed independently,...

In a series of papers, Hiriart-Urruty proposed necessary and sufficient global optimality conditions for the so-called d.c. problem and the convex maximization problem. In this paper, we investigate the underlying local optimality conditions, which, in general, are necessary, but not sufficient...

We develop a duality theory for weakly minimal points of multiple objective linear programs which has several advantages in contrast to other theories. For instance, the dual variables are vectors rather than matrices and the dual feasible set is a polyhedron. We use a set-valued dual objective...

We consider vector optimization problems on Banach spaces without convexity assumptions. Under the assumption that the objective function is locally Lipschitz we derive Lagrangian necessary conditions on the basis of Mordukhovich subdifferential and the approximate subdifferential by Ioffe using...

Algorithms generating piecewise linear approximations of the nondominated set for general, convex and nonconvex, multicriteria programs are developed. Polyhedral distance functions are used to construct the approximation and evaluate its quality. The functions automatically adapt to the problem...

The problem of optimizing a biconvex function over a given (bi)convex or compact set frequently occurs in theory as well as in industrial applications, for example, in the field of multifacility location or medical image registration. Thereby, a function $$f:X\times Y\to{\mathbb{R}}$$ is called...