Concurrent stochastic methods for global optimization

The global optimization problem, finding the lowest minimizer of a nonlinear function of several variables that has multiple local minimizers, appears well suited to concurrent computation. This paper presents a new parallel algorithm for the global optimization problem. The algorithm is a stochastic method related to the multi-level single-linkage methods of Rinnooy Kan and Timmer for sequential computers. Concurrency is achieved by partitioning the work of each of the three main parts of the algorithm, sampling, local minimization start point selection, and multiple local minimizations, among the processors. This parallelism is of a coarse grain type and is especially well suited to a local memory multiprocessing environment. The paper presents test results of a distributed implementation of this algorithm on a local area network of computer workstations. It also summarizes the theoretical properties of the algorithm

Leveraging age diversity in times of demographic change: the crucial role of leadership

With demographic change, organizations today are seeing changes in societal make-up translated to the composition of their workforce. In the future, younger and older employees will have to work together synergistically to achieve good performance. The authors argue that it will be largely up to leaders to prevent the negative effects of age diversity, i.e. social categorization and intergroup bias, and to facilitate the positive effects of age diversity, i.e. the sharing of unique knowledge resources held by young and old. The authors argue that certain leadership behaviors and especially their combinations have great promise in leading diverse teams, and highlight why they should be used in conjunction with positive beliefs about diversity