Showing papers by "Abraham Charnes published in 1986"
TL;DR: In this article, an overall measure of efficiency is also obtained for each DMU from the observed values of its multiple inputs and outputs without requiring uses of a priori weights using parametric forms relating inputs to outputs.
149 citations
TL;DR: In this paper, ordinary least squares and least squares combined with ridge regressions are examined and compared with ordinary goal programming and goal programming combined with constraints on regressand values that admissible statistical estimates must satisfy.
43 citations
TL;DR: In this article, a goal-focusing approach was developed for the detailed quantitative analysis of intergenerational income transfers of a national social security system, which achieves both the trade-off analyses of utility function methods at Pareto-efficient points and due accounting for the effects of multiple objectives.
Abstract: In place of classical utility conceptions, policy discussions for intergenerational transfers of income involve the balancing of several desirable social goals: maintaining or improving the standard of living of (i) the working popuuion, (ii) the retired population, and (iii) maintaining a stable intergenerational transfer system. The new method of goal focusing achieves both the trade-off analyses of utility function methods at Pareto-efficient points and due accounting for the effects of multiple objectives. A goal-focusing approach is herein developed for the detailed quantitative analysis of intergenerational income transfers of a national social security system.
9 citations
TL;DR: A new algorithm for the separable convex programming with linear constraints is discussed, based on the approximation of the objective function by at most two linear pieces in the neighborhood of the current feasible solution.
Abstract: We discuss a new algorithm for the separable convex programming with linear constraints. This is based on the approximation of the objective function by at most two linear pieces in the neighborhood of the current feasible solution. The two segments will be adaptively defined rather than predecided fixed grids. If3 furthermore, the objective function is differentiable, and one introduces a non-Archimedean infinitesimal, the algorithm generates a sequence of feasible solutions every cluster point of which is an optimal solution. Computational tests on the problem with up to 196 non-linear variables are presented
1 citations