A unified approach to radial, hyperbolic, and directional efficiency measurement in Data Envelopment Analysis

The paper analyzes properties of a large class of "path-based" Data Envelopment Analysis models through a unifying general scheme, which includes as standard the well-known oriented radial models, the hyperbolic distance measure model, and the directional distance measure models. The scheme also accommodates variants of standard models over negative data. Path-based models are analyzed from the point of view of nine desired properties that a well-designed model should satisfy. The paper develops mathematical tools that allow systematic investigation of these properties in the general scheme including, but not limited to, the standard path-based models. Among other results, the analysis confirms the generally accepted view that path-based models need not generate Pareto--Koopmans efficient projections, one-to-one identification, or strict monotonicity

A strategy of global convergence for the affine scaling algorithm for convex semidefinite programming

The affine scaling algorithm is one of the earliest interior point methods developed for linear programming. This algorithm is simple and elegant in terms of its geometric interpretation, but it is notoriously difficult to prove its convergence. It often requires additional restrictive conditions such as nondegeneracy, specific initial solutions, and/or small step length to guarantee its global convergence. This situation is made worse when it comes to applying the affine scaling idea to the solution of semidefinite optimization problems or more general convex optimization problems. In (Math Program 83(1–3):393–406, 1998), Muramatsu presented an example of linear semidefinite programming, for which the affine scaling algorithm with either short or long step converges to a non-optimal point. This paper aims at developing a strategy that guarantees the global convergence for the affine scaling algorithm in the context of linearly constrained convex semidefinite optimization in a least restrictive manner. We propose a new rule of step size, which is similar to the Armijo rule, and prove that such an affine scaling algorithm is globally convergent in the sense that each accumulation point of the sequence generated by the algorithm is an optimal solution as long as the optimal solution set is nonempty and bounded. The algorithm is least restrictive in the sense that it allows the problem to be degenerate and it may start from any interior feasible point