HRM higher education in Europe

Higher education has earned in the recent years an increasing attention in Europe. European Higher Education Area (EHEA) is being developed, a three-cycle qualifications structure is formally in place, the principles of quality assurance are agreed, and a range of mobility instruments is being created. A European Qualifications Framework (EQF) is established (2008), and national qualifications frameworks in Europe should be all be published this year. In the HRM field, higher education is remaining largely unexamined. It is undertheorised and with lack of empirical research, especially if we consider the European reality. With the exception of Brewster et al. (2000) and Boxall et al. (2007), all the research published on the specific HRM higher education field seems to be American (Barber, 1999; Chadwick, 2005; Hayton et al.,2005; Kaufman, 1996, 1999; Langbert, 2005, 2000; Sincoff & Owen, 2004; Van Eyden et al., 1997; Wimbush 2008). This study analyses HRM higher education in Europe with the aim to identify the current trends of European HRM academic qualifications system. In order to provide strategic indicators on the subject, research was carried out in 14 European countries. The sample was constituted by the three cycles defined in Bergen (bachelor, master and PhD) and the primary information source was the Portal on Learning Opportunities throughout the European Space promoted by the European Commission (PLOTEUS). Within a qualitative methodology, qualifications’ structures, approaches, and competencies are explored as main analytical categories.

A filter inexact-restoration method for nonlinear programming

A new iterative algorithm based on the inexact-restoration (IR) approach combined with the filter strategy to solve nonlinear constrained optimization problems is presented. The high level algorithm is suggested by Gonzaga et al. (SIAM J. Optim. 14:646–669, 2003) but not yet implement—the internal algorithms are not proposed. The filter, a new concept introduced by Fletcher and Leyffer (Math. Program. Ser. A 91:239–269, 2002), replaces the merit function avoiding the penalty parameter estimation and the difficulties related to the nondifferentiability. In the IR approach two independent phases are performed in each iteration, the feasibility and the optimality phases. The line search filter is combined with the first one phase to generate a “more feasible” point, and then it is used in the optimality phase to reach an “optimal” point. Numerical experiences with a collection of AMPL problems and a performance comparison with IPOPT are provided.