22 research outputs found
A Parametric Simplex Algorithm for Linear Vector Optimization Problems
In this paper, a parametric simplex algorithm for solving linear vector
optimization problems (LVOPs) is presented. This algorithm can be seen as a
variant of the multi-objective simplex (Evans-Steuer) algorithm [12]. Different
from it, the proposed algorithm works in the parameter space and does not aim
to find the set of all efficient solutions. Instead, it finds a solution in the
sense of Loehne [16], that is, it finds a subset of efficient solutions that
allows to generate the whole frontier. In that sense, it can also be seen as a
generalization of the parametric self-dual simplex algorithm, which originally
is designed for solving single objective linear optimization problems, and is
modified to solve two objective bounded LVOPs with the positive orthant as the
ordering cone in Ruszczynski and Vanderbei [21]. The algorithm proposed here
works for any dimension, any solid pointed polyhedral ordering cone C and for
bounded as well as unbounded problems. Numerical results are provided to
compare the proposed algorithm with an objective space based LVOP algorithm
(Benson algorithm in [13]), that also provides a solution in the sense of [16],
and with Evans-Steuer algorithm [12]. The results show that for non-degenerate
problems the proposed algorithm outperforms Benson algorithm and is on par with
Evan-Steuer algorithm. For highly degenerate problems Benson's algorithm [13]
excels the simplex-type algorithms; however, the parametric simplex algorithm
is for these problems computationally much more efficient than Evans-Steuer
algorithm.Comment: 27 pages, 4 figures, 5 table
PaMILO: A Solver for Multi-Objective Mixed Integer Linear Optimization and Beyond
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. 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
Two-phase strategies for the bi-objective minimum spanning tree problem
This paper presents a new two-phase algorithm for the bi-objective minimum spanning tree (BMST) prob-lem. In the first phase, it computes the extreme supported efficient solutions resorting to both mathematicalprogramming and algorithmic approaches, while the second phase is devoted to obtaining the remaining ef-ficient solutions (non-extreme supported and non-supported). This latter phase is based on a new recursiveprocedure capable of generating all the spanning trees of a connected graph through edge interchanges basedon increasing evaluation of non-zero reduced costs of associated weighted linear programs. Such a procedureexploits a common property of a wider class of problems to which the minimum spanning tree (MST) prob-lem belongs, that is the spanning tree structure of its basic feasible solutions. Computational experimentsare conducted on different families of graphs and with different types of cost. These results show that thisnew two-phase algorithm is correct, very easy to implement and it allows one to extract conclusions on thedifficulty of finding the entire set of Pareto solutions of the BMST problem depending on the graph topologyand the possible correlation of the edge cost
A Comparison of Benson’s Outer Approximation Algorithm with an Extended Version of Multiobjective Simplex Algorithm
The multiple objective simplex algorithm and its variants work in the decision variable space to find the set of all efficient extreme points of multiple objective linear programming (MOLP). Other approaches to the problem find either the entire set of all efficient solutions or a subset of them and also return the corresponding objective values (nondominated points). This paper presents an extension of the multiobjective simplex algorithm (MSA) to generate the set of all nondominated points and no redundant ones. This extended version is compared to Benson’s outer approximation (BOA) algorithm that also computes the set of all nondominated points of the problem. Numerical results on nontrivial MOLP problems show that the total number of nondominated points returned by the extended MSA is the same as that returned by BOA for most of the problems considered
An outer approximation algorithm for multi-objective mixed-integer linear and non-linear programming
In this paper, we present the first outer approximation algorithm for
multi-objective mixed-integer linear programming problems with any number of
objectives. The algorithm also works for certain classes of non-linear
programming problems. It produces the non-dominated extreme points as well as
the facets of the convex hull of these points. The algorithm relies on an
oracle which solves single-objective weighted-sum problems and we show that the
required number of oracle calls is polynomial in the number of facets of the
convex hull of the non-dominated extreme points in the case of multiobjective
mixed-integer programming (MOMILP). Thus, for MOMILP problems for which the
weighted-sum problem is solvable in polynomial time, the facets can be computed
with incremental-polynomial delay. From a practical perspective, the algorithm
starts from a valid lower bound set for the non-dominated extreme points and
iteratively improves it. Therefore it can be used in multi-objective
branch-and-bound algorithms and still provide a valid bound set at any stage,
even if interrupted before converging. Moreover, the oracle produces Pareto
optimal solutions, which makes the algorithm also attractive from the primal
side in a multi-objective branch-and-bound context. Finally, the oracle can
also be called with any relaxation of the primal problem, and the obtained
points and facets still provide a valid lower bound set. A computational study
on a set of benchmark instances from the literature and new non-linear
multi-objective instances is provided.Comment: 21 page