A unifying framework for duality and modeling in robust linear programs

In this paper, our major theme is a unifying framework for duality in robust linear programming. We show that there are two pair of dual programs allied with a robust linear program; one in which the primal is constructed to be “ultra-conservative” and one in which the primal is constructed to be “ultra-optimistic.” Furthermore, as one would expect, if the uncertainly in the primal is row-based, the corresponding uncertainty in the dual is column-based, and vice-versa. Several examples are provided that illustrate the properties of these primal and dual models.A second theme of the paper is about modeling in robust linear programming. We replace the ordinary activity vectors (points) and right-hand sides with well-known geometric objects such as hyper-rectangles, parallel line segments and hyper-spheres. In this manner, imprecision and uncertainty can be explicitly modeled as an inherent characteristic of the model. This is in contrast to the usual approach of using vectors to model activities and/or constraints and then, subsequently, imposing some further constraints in the model to accommodate imprecision and uncertainties. The unifying duality structure is then applied to these models to understand and interpret the marginal prices. The key observation is that the optimal solutions to these dual problems are comprised of two parts: a traditional “centrality” component along with a “robustness” component.

► Column-based robust linear programs have both ultra-conservative and ultra-optimistic formulations. ► The paper provides a framework for duality theory for linear robust-optimization models. ► The dual of the ultra-optimistic formulation is the familiar row-based robust linear program. ► The paper illustrates the duality framework by solving and providing insight into the classic portfolio selection problem. ► The paper illustrates how to use restricted shapes to model uncertainty with robust linear programs.