Ury Passy

Condensing generalized polynomials

An extension of geometric programming to includegeneralized polynomials, and not onlyPositive polynomials, is described, together with an algorithm and a numerical example.

Explicit Representation of the Two-Dimensional Section of a Production Possibility Set

Technical and scale efficiencies of Data Envelope Analysis are associated with a two dimensionalsection (a convex set) representing the amounts by which the input and output vectors of a reference decision making unit, may be scaled and still lie in the production possibility set. We describe a simple algorithm, closely resembling the simplex algorithm of linear programming, to traverse the boundary of this set. Given the output of our algorithm, the scalar efficiency measures and return-to-scale characterization are trivially determined. Moreover, the set may be graphically displayed for any problem in any number of dimensions with only a minimum of additional computing effort.

Projectively-convex sets and functions

Abstract We introduce a generalization of convexity called projective-convexity (or P-convexity) which extends models for consumer preference and consumer budgeting. We prove a separation theorem for projectively-convex sets, establish a duality theorem for projectively-concave technologies, and briefly analyse extremal properties of projectively-concave functions.

Lower Bound Restrictions on Intensities in Data Envelopment Analysis

We propose an extension to the basic DEA models that guarantees that if an intensity is positive then it must be at least as large as a pre-defined lower bound. This requirement adds an integer programming constraint known within Operations Research as a Fixed-Charge (FC) type of constraint. Accordingly, we term the new model DEA_FC. The proposed model lies between the DEA models that allow units to be scaled arbitrarily low, and the Free Disposal Hull model that allows no scaling. We analyze 18 datasets from the literature to demonstrate that sufficiently low intensities—those for which the scaled Decision-Making Unit (DMU) has inputs and outputs that lie below the minimum values observed—are pervasive, and that the new model ensures fairer comparisons without sacrificing the required discriminating power. We explain why the “low-intensity” phenomenon exists. In sharp contrast to standard DEA models we demonstrate via examples that an inefficient DMU may play a pivotal role in determining the technology. We also propose a goal programming model that determines how deviations from the lower bounds affect efficiency, which we term the trade-off between the deviation gap and the efficiency gap.

A duality approach to minimax results for quasi-saddle functions in finite dimensions

In this paper we show how saddle point theorems for a quasiconvex—quasiconcave function can be derived from duality theory. A symmetric duality framework that provides the machinery for deriving saddle point theorems is presented. Generating the theorems,via the framework, provides a deeper understanding of assumptions employed in existing theorems which do not utilize duality theory.

Global Solutions of Mathematical Programs with Intrinsically Concave Functions

An implicit enumeration technique for solving a certain type of nonconvex program is described. The method can be used for solving signomial programs with constraint functions defined by sums of quasiconcave functions and other types of programs with constraint functions called intrinsically concave functions. A signomial-type example is solved by this method. The algorithm is described together with a convergence proof. No computational results are available at present.

Analysis of multiobjective decision problems by the indifference band approach

A new approach, based on indifference band, for analyzing problems with multiple objectives is described. The relations of this approach to some previous results are given. Methods for generating nondominated solutions are supplied.

Efficiency estimation and duality theory for nonconvex technologies

Abstract We develop a symmetric, reflexive duality for nonconvex technologies. Our results make it possible to estimate organizational inefficiency, and to decompose this inefficiency into its managerial and allocative components.

Local-global properties of bifunctions

Representations of composite systems, such as bilinear programming, models of consumer/producer behavior, and sensitivity problems involve bifunctions (functions of two vector arguments). Such bifunctions are typically convex, pseudoconvex, or quasiconvex in each of their arguments, but not jointly convex, pseudoconvex, or quasiconvex. These functions do not in general possess the strong local-global property, namely, that every stationary point is a global minimum. In this paper, we define conditions that ensure that a bifunction possesses only a global minimum. In exploring this question, we use P-convexity and pseudo P-convexity, which are classes of bifunctions that generalize quasiconvexity and pseudoconvexity.

Duality for a class of quasiconvex programs

This paper derives a duality relation between a pair of explicitly quasiconvex and quasiconcave programs.