2022/07/19 by Fritz Bökler, Bökler, Fritz, Levin Nemesch +3 · 1 citation
Engineering · Mathematics · #Advanced Control Systems Optimization #Advanced Optimization Algorithms Research #Discrete Mathematics (cs.DM) #FOS: Computer and information sciences #FOS: Mathematics #Mathematical Software (cs.MS) #Optimization and Control (math.OC) #Process Optimization and Integration
paper · pdf · doi:10.48550/arxiv.2207.09155
openalex publication_date 2022/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In multi-objective optimization, several potentially conflicting objective functions need to be optimized. Instead of one optimal solution, we look for the set of so called non-dominated solutions. An important subset is the set of non-dominated extreme points. Finding it is a computationally hard problem in general. While solvers for similar problems exist, there are none known for multi-objective mixed integer linear programs (MOMILPs) or multi-objective mixed integer quadratically constrained quadratic programs (MOMIQCQPs). We present PaMILO, the first solver for finding non-dominated extreme points of MOMILPs and MOMIQCQPs. It can be found on github under github.com/FritzBo/PaMILO. PaMILO provides an easy-to-use interface and is implemented in C++17. It solves occurring subproblems employing either CPLEX or Gurobi. PaMILO adapts the Dual-Benson algorithm for multi-objective linear programming (MOLP). As it was previously only defined for MOLPs, we describe how it can be adapted for MOMILPs, MOMIQCQPs and even more problem classes in the future.