Showing papers by "William W. Cooper published in 1980"
TL;DR: A measure of efficiency for not-for-profit entities is explained and illustrated by data from Program Follow Through, a large scale social experiment in U.S. public school education as discussed by the authors.
Abstract: A measure of efficiency for not-for-profit entities - developed by the authors in association with Edward Rhodes - is explained and illustrated by data from Program Follow Through, a large scale social experiment in U.S. public school education. A division into Follow Through and Non-Follow Through participants facilitates a distinction between “program efficiency” and “managerial efficiency” which is also illustrated and examined for its use in evaluating such programs. Relations to comprehensive audits and other possible uses are explored.
54 citations
TL;DR: A method is presented for obtaining minimum discrimination information estimates of probability distributions using an extremal principle of Charnes and Cooper (1974) and, viewing M.D.I, estimation in a dual convex programming framework.
Abstract: A method is presented for obtaining minimum discrimination information (M.D.I.) estimates of probability distributions. This involves using an extremal principle of Charnes and Cooper (1974) and, viewing M.D.I, estimation in a dual convex programming framework. The resulting dual convex program is unconstrained and involves only exponential and linear terms, and hence is easily
33 citations
TL;DR: It is concluded that the “imposed problem ignorance” of past complexity research is deleterious to research progress on “computability” or “efficiency of computation.”
Abstract: Through key examples and constructs, exact and approximate, complexity, computability, and solution of linear programming systems are reexamined in the light of Khachian's new notion of (approximate) solution. Algorithms, basic theorems, and alternate representations are reviewed. It is shown that the Klee-Minty example hasnever been exponential for (exact) adjacent extreme point algorithms and that the Balinski-Gomory (exact) algorithm continues to be polynomial in cases where (approximate) ellipsoidal “centered-cutoff” algorithms (Levin, Shor, Khachian, Gacs-Lovasz) are exponential. By “model approximation,” both the Klee-Minty and the new J. Clausen examples are shown to be trivial (explicitly solvable) interval programming problems. A new notion of computable (approximate) solution is proposed together with ana priori regularization for linear programming systems. New polyhedral “constraint contraction” algorithms are proposed for approximate solution and the relevance of interval programming for good starts or exact solution is brought forth. It is concluded from all this that the “imposed problem ignorance” of past complexity research is deleterious to research progress on “computability” or “efficiency of computation.”
7 citations