Validation of Linear Programming Models

Xviii : Validation of Programming Models

Using Output of Linear Programming-based Models

Among classification problems multi-modal problems form an important category. They naturally arise in many fields when a single class can occupy disjoint areas in the feature space. They are particularly hard to solve for traditional classification techniques. Decision tree Classifiers(DTCs) in principle are able to solve such problems by the recursive nature of their algorithm. In principle, ...

Three models of fuzzy integer linear programming

In this paper we study some models for dealing with Fuzzy Integer Linear Programming problems which have a certain lack of precision of a vague nature in their formulation. We present methods to solve them with either fuzzy constraints, or fuzzy numbers in the objective function or fuzzy numbers defining the set of constraints. These methods are based on the representation theorem and on fuzzy ...

Integer Linear Programming Models for Constrained Clustering

We address the problem of building a clustering as a subset of a (possibly large) set of candidate clusters under user-defined constraints. In contrast to most approaches to constrained clustering, we do not constrain the way observations can be grouped into clusters, but the way candidate clusters can be combined into suitable clusterings. The constraints may concern the type of clustering (e....

Optimal portfolios using linear programming models

The classical Quadratic Programming formulation of the well known portfolio selection problem, is cumbersome, time consuming and relies on two important assumptions: (a) the expected return is multivariate normally distributed; (b) the investor is risk averter. This paper formulates two alternative models, (i) maximin, and (ii) minimization of absolute deviation. Data from a very simple problem...