We present a branch-and-bound algorithm for minimizing multiple convex quadratic objective functions over integer variables. Our method looks efficient points by fixing subsets of variables to values and using lower bounds in the form hyperplanes image space derived from continuous relaxations restricted functions. show that stops after finitely many fixings with detecting both full nondominate...

The investigation of the performance of the Particle Swarm Optimization (PSO) method in Integer Programming problems, is the main theme of the present paper. Three variants of PSO are compared with the widely used Branch and Bound technique, on several Integer Programming test problems. Results indicate that PSO handles e ciently such problems, and in most cases it outperforms the Branch and Bo...

Polygonal approximation is an effective yet challenging digital curve representation for image analysis, pattern recognition and computer vision. This paper proposes a novel approach, integer particle swarm optimization (iPSO), for polygonal approximation. When compared to the traditional binary version of particle swarm optimization (bPSO), the new iPSO directly uses an integer vector to repre...

This paper describes ILP-PLAN, a framework for solving AI planning problems represented as integer linear programs. ILP-PLAN extends the planning as satisfiability framework to handle plans with resources, action costs, and complex objective functions. We show that challenging planning problems can be effectively solved using both traditional branch-and-bound integer programming solvers and eff...

Given a set of target detections over several time periods, this paper addresses the multi-target tracking problem (MTT) of optimally assigning detections to targets and estimating the trajectory of the targets over time. MTT has been studied in the literature via predominantly probabilistic methods. In contrast to these approaches, we propose the use of mixed integer optimization (MIO) models ...

We show that if one can find the optimal value of an integer linear programming problem in polynomial time, then one can find an optimal solution in polynomial time. We also present a proper generalization to (general) integer programs and to local search problems of the well-known result that optimization and augmentation are equivalent for 0/1-integer programs. Among other things, our results...

We show that Integer Programming (IP) can be used as an optimization technique for the instantiation of products of feature models. This is done by showing that the constraints of feature models can be written in linear form. As particular IP technique, we use Gomory cutting planes. We have applied this technique to a test suite of feature models from the literature, and found that the Gomory c...

We present a flexible framework for general mixed-integer nonlinear programming (MINLP), called Minotaur, that enables both algorithm exploration and structure exploitation without compromising computational efficiency. This paper documents the concepts and classes in our framework and shows that our implementations of standard MINLP techniques are efficient compared with other state-of-the-art...