In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated where the feasible region is formed as the intersection of two inequality fuzzy systems and Dombi family of t-norms is considered as fuzzy composition. Dombi family of t-norms includes a parametric family of continuous strict t-norms, whose members are increasing functions of ...

One of the main techniques used in data mining is data clustering, which has many applications in computer science, biology, and social sciences. Constrained clustering is a type of clustering in which side information provided by the user is incorporated into current clustering algorithms. One of the well researched constrained clustering algorithms is called microaggregation. In a microaggreg...

  Semidefinite optimization relaxations are among the widely used approaches to find global optimal or approximate solutions for many nonconvex problems. Here, we consider a specific quadratically constrained quadratic problem with an additional linear constraint. We prove that under certain conditions the semidefinite relaxation approach enables us to find a global optimal solution of the unde...

We develop a method to obtain an optimal solution for a constrained distribution system with several items and multi-retailers. The objective is to determine the procurement frequency as well as the joint shipment interval for each retailer in order to minimize the total costs. The proposed method is applicable to both nested and non-nested policies and ends up with an optimal solution. To solv...

The paper aims at contrasting two different ways of incorporating a-priori information in parameter estimation, i.e. hard-constrained and soft-constrained estimation. Hardconstrained estimation can be interpreted, in the Bayesian framework, as Maximum A-posteriori Probability (MAP) estimation with uniform prior distribution over the constraining set, and amounts to a constrained Least-Squares (...