This paper deals with optimal partitioning of limited testing time in order to achieve maximum total test score. Nonlinear optimization theory was used to analyze this problem. A general case using a generic item response model is first presented. A special case that applies a response time model proposed by Wang and Hanson (2005) is also presented. Theoretical properties of the optimal solution are derived. Their practical implications to optimal test-taking strategies are also discussed. The theoretical properties are in general agreement with the conventional advice to the examinees on pacing strategy.

In this study, highly practical and reliable methods are proposed to determine arm solutions for a six-link robot manipulator. Based on a typical mathematical structure of minimizing an objective function, the optimization theory is applied to solve a reduced system of kinematic equations. The performance tests show that three different approaches are superior to a conventional method and of sufficiently practical use. Especially, the use of an algorithm presented in a linear search is promising.

We consider the problem of maximizing the probability of choosing the largest from a sequence of N observations when N is a bounded random variable. The present paper gives a necessary and sufficient condition, based on the distribution of N, for the optimal stopping rule to have a particularly simple form: what Rasmussen and Robbins (1975) call an s(r) rule. A second result indicates that optimal stopping rules for this problem can, with one restriction, take virtually any form.

The familiar problem of maximizing the probability of choosing the best from a group of N candidates, where N is known, is extended to the case of N unknown. An a priori distribution is assumed for N, and the case of a uniform distribution is examined. Let VN denote the probability of choosing the best from a group of at most N candidates, then it is shown that limN→∞VN = 2e–2.