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This paper presents an investigation of the effects of errors in the estimation of the gradient of the objective function on the FRANK-WOLFE nonlinear programming algorithm. The investigation was motivated by a desire to reconcile the use of interactive programming approaches to the solution of the multiple-criteria problem with utility models that allow for intransitive statements of indifference and preference. Results are provided regarding the ultimate convergence of the algorithm, and its initial convergence rate. [ABSTRACT FROM AUTHOR]

In this paper it is assumed that, using a sample survey, two factors are to be studied, comparisons between the "levels" of the factors are of greatest interest, and there is "interaction" between the factors. Attention is concentrated on situations in which only two levels of each factor are to be compared, but extensions to more complex surveys are discussed. Assuming independent sampling, optimal sample size allocations are obtained. Where these allocations require recourse to programming algorithms, approximate solutions are given. If independent sampling is not feasible, a double sampling procedure is suggested. To indicate how sub-sampling from the first phase sample is to be carried out, a sampling rule (possessing optimal conditional precision properties) is derived. Then, a procedure to determine the optimal first phase sample size is given. Finally, it is demonstrated that this double sampling procedure can be applied to estimation of the (finite) population mean when double sampling with stratification is used. [ABSTRACT FROM AUTHOR]

This paper is devoted to a discussion of an iterative procedure for obtaining the maximum likelihood estimator of the covariance matrix for multivariate experiments in which measurements on certain components of a normal vector random variable are intentionally omitted in corresponding subgroups of the experimental units. The proposed algorithm, which is based on the Newton-Raphson method, is outlined in detail in Section 5. A special design is included to illustrate the procedure and a few areas of application are indicated. [ABSTRACT FROM AUTHOR]

The problems studied here may seem unrelated, although the analysis of each requires the use of the circular coverage function. The first considers an n-weapon attack on a circular area with uniform target density; the danaage function is based on HUNTER'S (1967). The second considers a single-weapon attack on a target area with a symmetric normal density of targets, using a generalized 'cookie-cutter' damage function. The damage functions used are believed to provide better fits to certain empirical damage curves than the cookie-cutter, and they have not previously been studied with the target models here. Algorithms are derived for calculating the expected number of casualties, or of targets damaged. Symmetric Gauseian aiming errors are assumed in both problems. [ABSTRACT FROM AUTHOR]

The article comments on the paper "Syminv: An Algorithm for the Inversion of a Positive Definite Matrix by the Cholesky Decomposition," by Terry Seaks, published in volume 40 of "Econometrica." In his article Seaks describes an algorithm for the Cholesky decomposition of a positive symmetric matrix, citing J.H. Wilkinson's work. The author explains that the relevant conclusions in Wilkinson's paper are those for floating point calculation and that the numerical advantage of the Cholesky procedure depends on being able to accumulate inner products accurately.