Cyber War is Inevitable (Unless We Build Security In)

The information systems controlling our critical infrastructure are vulnerable to cyber attack. Cyber war is therefore inevitable unless we improve our cyber defenses. The only way to do this is by building security into systems at the design stage.     

Virtual War in the Ice Jungle: ‘We don't know how to do this’

The Cold War at sea expanded in many realms, including the frigid and treacherous waters of the Arctic Ocean. The US Navy pioneered these efforts with the visit of USS Nautilus to the North Pole in 1958. During the latter stages of the Cold War, however, Soviet naval strategists began to conceptualize the polar ice cap as a strategic asset for cloaking the operations of its nuclear missile submarines. As under-ice operations afforded the Soviet submarine fleet advantages of stealth, proximity to target and tighter lines of communications, both navies were forced to try to develop tactics for combat under these extremely complex and arduous conditions.     

The conditional p-center problem in the plane

An algorithm is given for the conditional p-center problem, namely, the optimal location of one or more additional facilities in a region with given demand points and one or more preexisting facilities. The solution dealt with here involves the minimax criterion and Euclidean distances in two-dimensional space. The method used is a generalization to the present conditional case of a relaxation method previously developed for the unconditional p-center problems. Interestingly, its worst-case complexity is identical to that of the unconditional version, and in practice, the conditional algorithm is more efficient. Some test problems with up to 200 demand points have been solved. © 1993 John Wiley & Sons, Inc.     

Optimal clustering: A model and method

Classifying items into distinct groupings is fundamental in scientific inquiry. The objective of cluster analysis is to assign n objects to up to K mutually exclusive groups while minimizing some measure of dissimilarity among the items. Few mathematical programming approaches have been applied to these problems. Most clustering methods to date only consider lowering the amount of interaction between each observation and the group mean or median. Clustering used in information systems development to determine groupings of modules requires a model that will account for the total group interaction. We formulate a mixed-integer programming model for optimal clustering based upon scaled distance measures to account for this total group interaction. We discuss an efficient, implicit enumeration algorithm along with some implementation issues, a method for computing tight bounds for each node in the solution tree, and a small example. A computational example problem, taken from the computer-assisted process organization (CAPO) literature, is presented. Detailed computational results indicate that the method is effective for solving this type of cluster analysis problem.     

A graphical aid for sensitivity analysis of estimated regression coefficients

A problem often arising in applied linear regression modeling is determining the appropriate weight to attach to each observation. In the extreme, the problem extends to the deletion of observations from a data base. Since the choice of weights, fitting criteria, and estimation procedure depend on the specific objectives of the modeling, universally applicable guidelines are virtually nonexistent. However, the sensitivity of analytic conclusions to weights assigned to “suspect” observations can be conveniently assessed using a graphical display. This report develops such a display based on a modeling of outliers which leads naturally to estimators based on a weighted least-squares criterion, and a data-analytic method for determining how much downweighting to impose on a specific subset of observations. This technique is illustrated with several examples, including one relating air pollution to human mortality.     

Postoptimality analysis in zero-one programming by implicit enumeration

The procedures for postoptimality analysis that are so much a part of linear programming studies have no simple counterparts in an integer programming context. In the case of Balas' Additive Algorithm, however, it would appear that the capacity of the technique to facilitate certain types of postoptimality analysis has not been fully exploited. This paper proposes an extension of the additive algorithm that utilizes insights generated while solving the original problem to do subsequent analysis upon it. In particular, procedures are developed for doing limited parameter ranging and for seeking new optima in light of parameter changes.     

Determining restrictive goals in linear goal programs

Goal programming is a method for solving problems having multiple, conflicting objectives. Information on the characteristics of the goals can help in setting goal weights and levels, determining priority levels, and in conducting postoptimality analysis. One characteristic affecting the outcome of a goal program is the restrictiveness of a goal. A restrictive goal is one whose inclusion in the goal set most degrades the value of the total objective function, or the attainment of the remaining goals. An algorithm is presented that determines restrictive goals.     

The complementary use of regression diagnostics and robust estimators

Regression modeling for prediction or forecasting purposes is critically dependent on the quality of the data which are used to estimate the model parameters. Extreme response or predictor-variable values can substantially influence least-squares estimates and disproportionately affect predictions. Robust alternatives to least-squares are less sensitive to extreme observations and can provide more precise predictions. In this article diagnostic displays are used to identify extreme observations and to assess the sensitivity of least-squares parameter estimates and predictions to the inclusion of these observations in a data set. The displays are shown to aid in the interpretation of weights which robust estimators assign to influential observations.