Computational study of the step size parameter of the subgradient optimization method

The subgradient optimization method is a simple and flexible linear programming iterative algorithm. It is much simpler than Newton's method and can be applied to a wider variety of problems. It also converges when the objective function is non-differentiable. Since an efficient algorithm will not only produce a good solution but also take less computing time, we always prefer a simpler algorithm with high quality. In this study a series of step size parameters in the subgradient equation is studied. The performance is compared for a general piecewise function and a specific p-median problem. We examine how the quality of solution changes by setting five forms of step size parameter.

The sustainability revolution : A societal paradigm shift - ethos, innovation, government transformation

The Sustainability revolution: A societal paradigm shift – ethos, innovation, governance transformation This paper identifies several key mechanisms that underlie major paradigm shifts. After identifying four such mechanisms, the article focuses on one type of transformation which has a prominent place in the sustainability revolution that the article argues is now taking place. The transformation is piecemeal, incremental, diffuse – in earlier writings referred to as ”organic”. This is a more encompassing notion than grassroots, since the innovation and transformation processes may be launched and developed at multiple levels through diverse mechanisms of discovery and development. Major features of the sustainability revolution are identified and comparisons made to the industrial revolution.

Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles : a simulation study

Generalized linear mixed models are flexible tools for modeling non-normal data and are useful for accommodating overdispersion in Poisson regression models with random effects. Their main difficulty resides in the parameter estimation because there is no analytic solution for the maximization of the marginal likelihood. Many methods have been proposed for this purpose and many of them are implemented in software packages. The purpose of this study is to compare the performance of three different statistical principles - marginal likelihood, extended likelihood, Bayesian analysis-via simulation studies. Real data on contact wrestling are used for illustration.