The linear programming formulations of model predictive control are known to exhibit degenerate solution behavior. In this work, a multi-parametric linear programming technique is utilized to analyze the control laws that are generated from various linear programming based MPC routines. These various routines explore a number of factors, including objective function selection and constraint han...

Although many programs use multi-dimensional arrays, the multi-dimensional view of data is often not directly visible in the internal representation used by LLVM. In many situations, the only information available is an array base pointer and a single dimensional offset. For problems with parametric size, this offset is usually a multivariate polynomial that cannot be analyzed with integer line...

In this paper we study the problem of parametric minimization of convex piecewise quadratic functions. Our study provides a unifying framework for convex parametric quadratic and linear programs. Furthermore, it extends parametric programming algorithms to problems with piecewise quadratic cost functions, paving the way for new applications of parametric programming in dynamic programming and o...

in traditional portfolio selection model coefficients often are certain and deterministic, but in real world these coefficients are probabilistic. so decision maker cannot estimate them exactly. financial optimization is one of the most attractive areas in decision under uncertainty. in the portfolio selection problem the decision maker considers simultaneously conflicting objectives such as ra...

Scenario analysis within a multi-stage stochastic programming formulation offers an attractive framework for modelling uncertainties in long-range planning models. However, whether the expected outcome is implicitly / explicitly evaluated, such formulations lead to computationally intensive optimization problems. Focussing here on the scenario planning / multi-period approach for mixed integer ...

data envelopment analysis (dea) is a technique used to compare efficiency in various sectors such as hospitals, chain stores, and dealerships. it represents a set of linear programming techniques and uses deter-ministic data (inputs and outputs), in stable conditions. the dea technique cannot be used when there is data with indeterministic nature, or when there is an environment with dynamic co...

In real world applications the supply, the demand and the transportation cost per unit of the quantities in a transportation problem are hardly specified precisely because of the changing economic and environmental conditions. It is also important that the time required for transportation should be minimum. In this paper a method has been proposed for the minimization of transportation cost as ...

This paper explores the use of a non-parametric frontier approach to analyse multi-factor productivity across time and countries. We argue that conventional measures of total factor productivity involve some restrictive assumptions that might bias the results. A non-parametric approach avoids these assumptions. The model uses linear programming techniques to examine the productivity catching-up...

Soft computing techniques have been widely used during the last two decades for nonlinear system modeling, specifically as predictive tools. In this study, the performances of two well-known soft computing predictive techniques, artificial neural network (ANN) and genetic programming (GP), are evaluated based on several criteria, including over-fitting potential. A case study in punching shear ...