A Rejoinder to Peter M. Sales

This analytical article asks the question: to what degree did the media contribute to the Rwandan genocide and what might have been done about it? In examining the historical development of mass media in Rwanda, this paper argues that while hate media clearly contributed to the dynamics that led to genocide, its role should not be overstated. While it is commonly believed that hate media was a major cause of the genocide, instead it was a part of a larger social process. The use of violent discourse was at least as important as, for example, the availability of weapons in carrying out the genocide. Put another way, violent discourse was necessary but not sufficient by itself to cause the genocide of 1994. In arguing this thesis, Rwandan history is examined to demonstrate the processes of communication in the formation of destructive attitudes and behaviour. Next, analysis of the methods and content of propaganda campaigns is discussed. Finally, an overview of the requirements and organizations for third parties to conduct international communication interventions is presented in the last section.     

An advanced dual algorithm with constraint relaxation for all-integer programming

In this article we present an all-integer cutting plane algorithm called the Reduced Advanced Start Algorithm (RASA). The technique incorporates an infeasible advanced start based on the optimal solution to the LP relaxation, and initially discards nonbinding constraints in this solution. We discuss the results of computational testing on a set of standard problems and illustrate the operation of the algorithm with three small examples.     

A bounded dual (all-integer) integer programming algorithm with an objective cut

In this article, we describe a new algorithm for solving all-integer, integer programming problems. We generate upper bounds on the decision variables, and use these bounds to create an advanced starting point for a dual all-integer cutting plane algorithm. In addition, we use a constraint derived from the objective function to speed progress toward the optimal solution. Our basic vehicle is the dual all-integer algorithm of Gomory, but we incorporate certain row- and column-selection criteria which partially avoid the problem of dual-degenerate iterations. We present the results of computational testing.     

The conjugate gradient technique for certain quadratic network problems

We consider a class of network flow problems with pure quadratic costs and demonstrate that the conjugate gradient technique is highly effective for large-scale versions. It is shown that finding a saddle point for the Lagrangian of an m constraint, n variable network problem requires only the solution of an unconstrained quadratic programming problem with only m variables. It is demonstrated that the number of iterations for the conjugate gradient algorithm is substantially smaller than the number of variables or constraints in the (primal) network problem. Forty quadratic minimum-cost flow problems of various sizes up to 100 nodes are solved. Solution time for the largest problems (4,950 variables and 99 linear constraints) averaged 4 seconds on the CBC Cyber 70 Model 72 computer.     

A semieconomic method for the determination of optimal lot sizes

The construction of lot sizes usually depends upon factors influencing homogeneity. When these factors are not a function of lot quantity, it is possible to determine an optimal lot size. The optimization process balances the cost of sampling against the expected cost of lot rejection for some specified procurement quantity. The rationale for balancing the two costs is contingent upon the fact that rejection criteria waivers frequently occur when the lot size is large. This concept implies that the lot size should be as small as possible, whereas the cost of sampling drives the lot size up. Hence, trade-offs may be made. The formulation is termed a semieconomic one because it combines a pure economic objective function with a pure statistical constraint. This constraint is necessary because the nature of the items under study dictates that the cost of accepting defective material cannot be explicitly stated. The paper presents the formulation, describes when it should be used, derives a good analytical approximation under certain assumptions and gives various ramifications when it is used.     

A primal-dual cutting-plane algorithm for all-integer programming

The integer programming literature contains many algorithms for solving all-integer programming problems but, in general, existing algorithms are less than satisfactory even in solving problems of modest size. In this paper we present a new technique for solving the all-integer, integer programming problem. This algorithm is a hybrid (i.e., primal-dual) cutting-plane method which alternates between a primal-feasible stage related to Young's simplified primal algorithm, and a dual-infeasible stage related to Gomory's dual all-integer algorithm. We present the results of computational testing.