Lagrangian relaxation is a powerful bounding technique that has been applied successfully to many / /9-hard combinatorial optimization problems. The basic idea is to see an ./K~-hard problem as an "easy-to-solve" problem complicated by a number of "nasty" side constraints. We show that reformulating nasty inequality constraints as equalities by using slack variables leads to stronger lower boun...

Research on musical instrument design suggests that deliberate constraints can offer new creative experiences to the performer. At times, design constraints are physically embedded in the instrument to limit the interaction possibilities; in other cases the constraints are given by delegating part of the control to the machine. In this paper we propose a case study related to the latter form of...

introduction: the critical dependence of healthcare services systems on information along with the indigenous restriction of paper documents in satisfying this need has caused a trend toward computer information systems. the main goal of such systems is to achieve electronic health records (ehr). however, implementation of ehr in healthcare organizations is difficult and complicated. this resea...

In this paper, we present a new MPEC model for calculating the optimal value of cost parameter C for particular problems of linear non-separability of data. The objective function of the new model is an integer lower semi-continuous one. Smoothing technique is employed for solving this model, and the relationship between the MPEC model and its associated smoothing problem is given. It is proved...

Fair classification is an emerging and important research topic in machine learning community. Existing methods usually formulate the fairness metrics as additional inequality constraints, then embed them into original objective. This makes fair problems unable to be effectively tackled by some solvers specific unconstrained optimization. Although many new tailored algorithms have been designed...