4 papers
Efficiently Constructing Convex Approximation Sets in Multiobjective Optimization Problems
Stephan Helfrich, Stefan Ruzika, Clemens Thielen
Convex approximation sets for multiobjective optimization problems are a well-studied relaxation of the common notion of approximation sets. Instead of approximating each image of…
Using Scalarizations for the Approximation of Multiobjective Optimization Problems: Towards a General Theory
Stephan Helfrich, Arne Herzel, Stefan Ruzika +1
We study the approximation of general multiobjective optimization problems with the help of scalarizations. Existing results state that multiobjective minimization problems can be…
Approximating Multiobjective Optimization Problems: How exact can you be?
Cristina Bazgan, Arne Herzel, Stefan Ruzika +2
It is well known that, under very weak assumptions, multiobjective optimization problems admit -approximation sets (also called -P…
Efficient Maximum-Likelihood Decoding of Linear Block Codes on Binary Memoryless Channels
Michael Helmling, Eirik Rosnes, Stefan Ruzika +1
In this work, we consider efficient maximum-likelihood decoding of linear block codes for small-to-moderate block lengths. The presented approach is a branch-and-bound algorithm us…