We present a new adaptive algorithm for convex quadratic multicriteria optimization. The algorithm is able to adaptively refine the approximation to the set of efficient points by way of a warm-start interior-point scalarization approach. Numerical results show that this technique is faster than a standard method used for this problem.

Scalarization approaches to purposely generating epsilon-efficient solutions of multiobjective programs are investigated and a generic procedure for computing these solutions is proposed and illustrated with an example. Real-life decision making situations in which the solutions are of significance are described.

The weighted sum method of vector objective scalarization is known to generate points on convex Pareto front whose distribution cannot be controlled. This work presents a method of improving the distribution of Pareto points generated by weighted sum method by nonlinear weight selection. Numerical examples are presented to show the effectiveness of the method.

In this paper, we study the spontaneous scalarization of an extended, self-gravitating system which is static, cylindrically symmetric and possesses electromagnetic fields. We demonstrate that a real massive scalar field condenses on Melvin magnetic universe solution when introducing non-minimal coupling between (a) (b) curvature space-time, respectively. find in both cases, solutions exist fin...

We propose an algorithm to generate inner and outer polyhedral approximations the upper image of a bounded convex vector optimization problem. It is approximation based on solving norm-minimizing scalarizations. Unlike Pascoletti–Serafini scalarization used in literature for similar purposes, it does not involve direction parameter. Therefore, free direction-biasedness. also modification by int...