Hong Kong Polytechnic University

About: Hong Kong Polytechnic University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Computer science & Tourism. The organization has 29633 authors who have published 72136 publications receiving 1956312 citations. The organization is also known as: HKPU & PolyU.

A review of transfer of training studies in the past decade

Abstract: This article reviews some major studies that were conducted in the past decade (1989‐1998) on the transfer of what employees learned from training programmes back to their jobs. A conceptual framework is developed for this article to better present the “popular” constructs that have been tested empirically. The achievement is twofold. First, this review paper highlights that some individual, motivational and environmental factors are related to transfer of training. Second, some directions for further studies have been suggested. For example, longitudinal study was highly recommended for measuring transfer outcomes. Some new individual (e.g. achievement striving), motivational (e.g. trainee‐control‐over‐training) and environmental (e.g. transfer climate) constructs are recommended to be incorporated in newly created models. These models can then be examined using structural equation modelling. After extensive testing and refinement of these models, a set of critical constructs can be distilled. By that time, convergence of research efforts focusing on major themes can be achieved.

Global Convergence of Splitting Methods for Nonconvex Composite Optimization

Abstract: We consider the problem of minimizing the sum of a smooth function $h$ with a bounded Hessian and a nonsmooth function. We assume that the latter function is a composition of a proper closed function $P$ and a surjective linear map $\mathcal{M}$, with the proximal mappings of $\tau P$, $\tau > 0$, simple to compute. This problem is nonconvex in general and encompasses many important applications in engineering and machine learning. In this paper, we examined two types of splitting methods for solving this nonconvex optimization problem: the alternating direction method of multipliers and the proximal gradient algorithm. For the direct adaptation of the alternating direction method of multipliers, we show that if the penalty parameter is chosen sufficiently large and the sequence generated has a cluster point, then it gives a stationary point of the nonconvex problem. We also establish convergence of the whole sequence under an additional assumption that the functions $h$ and $P$ are semialgebraic. Further...