A linear assignment formulation of the multiattribute decision problem
A linear programming based analysis of the CP-rank of completely positive matrices
A real matrix A is said to be completely positive (CP) if it can be decomposed as A = BB^T, where the real matrix B has exclusively non-negative entries. Let k be the rank of A and Φ_k the least possible number of columns of the matrix B, the so-called completely positive rank (cp-rank) of A. The present work is devoted to a study of a general upper bound for the cp-rank of an arbitrary completely positive matrix A and its dependence on the ordinary rank k. This general upper bound of the cp-rank...
A linear pseudo-Boolean viewpoint on matching and other central concepts in graph theory
A literature review on circle and sphere packing problems: models and methodologies.
A local and global search combine particle swarm optimization algorithm for job-shop scheduling to minimize makespan.
A localization problem in geometry and complexity of discrete programming
A logarithm barrier method for semi-definite programming
This paper presents a logarithmic barrier method for solving a semi-definite linear program. The descent direction is the classical Newton direction. We propose alternative ways to determine the step-size along the direction which are more efficient than classical line-searches.
A logarithmic barrier function method for solving nonlinear multiobjective programming problems
A Markov chain model for traffic equilibrium problems
We consider a stochastic approach in order to define an equilibrium model for a traffic-network problem. In particular, we assume a markovian behaviour of the users in their movements throughout the zones of the traffic area. This assumption turns out to be effective at least in the context of urban traffic, where, in general, the users tend to travel by choosing the path they find more convenient and not necessarily depending on the already travelled part. The developed model is a homogeneous Markov...
A Markov chain model for traffic equilibrium problems
We consider a stochastic approach in order to define an equilibrium model for a traffic-network problem. In particular, we assume a Markovian behaviour of the users in their movements throughout the zones of the traffic area. This assumption turns out to be effective at least in the context of urban traffic, where, in general, the users tend to travel by choosing the path they find more convenient and not necessarily depending on the already travelled part. The developed model is a homogeneous...
A measure of mutual divergence among a number of probability distributions.
A measure-theoretical max-flow-min-cut problem.
A Metaheuristic Approach to Solving the Generalized Vertex Cover Problem
AMS Subj. Classification: 90C27, 05C85, 90C59The topic is related to solving the generalized vertex cover problem (GVCP) by genetic algorithm. The problem is NP-hard as a generalization of well-known vertex cover problem which was one of the first problems shown to be NP-hard. The definition of the GVCP and basics of genetic algorithms are described. Details of genetic algorithm and numerical results are presented in [8]. Genetic algorithm obtained high quality solutions in a short period of time.
A method for finding common set of weights by multiple objective programming in data envelopment analysis.
A method of approximating Pareto sets for assessments of implicit Pareto set elements
A method of multiobjective decision making using a vector value function.
A decision situation with partial information on preferences by means of a vector value function is assumed. The concept of minimum value dispersion solution as a reference point joined with a pseudodistance function from such a point and a dispersion level ε, lead to the notion of ε-dispersion set. The dispersion level represents the amount of value that the decision maker can be indifferent to, therefore he should choose his most preferred solution in this set. Convergence properties, as well...
A method of reduction of constraints
A Mixed Integer Quadratic Programming Model for the Low Autocorrelation Binary Sequence Problem
In this paper the low autocorrelation binary sequence problem (LABSP) is modeled as a mixed integer quadratic programming (MIQP) problem and proof of the model’s validity is given. Since the MIQP model is semidefinite, general optimization solvers can be used, and converge in a finite number of iterations. The experimental results show that IQP solvers, based on this MIQP formulation, are capable of optimally solving general/skew-symmetric LABSP instances of up to 30/51 elements in a moderate time....
A model for a dynamic preventive maintenance policy.
A Modification of Revised Simplex Method