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.

Performance evaluation of ventilation radiators

A ventilation radiator is a combined ventilation and heat emission unit currently of interest due to its potential for increasing energy efficiency in exhaust ventilated buildings with warm water heating. This paper presents results of performance tests of several ventilation radiator models conducted under controlled laboratory conditions.   The purpose of the study was to validate results achieved by Computational Fluid Dynamics (CFD) in an earlier study and indentify possible improvements in the performance of such systems. The main focus was on heat transfer from internal convection fins, but comfort and health aspects related to ventilation rates and air temperatures were also considered.   The general results from the CFD simulations were confirmed; the heat output of ventilation radiators may be improved by at least 20 % without sacrificing ventilation efficiency or thermal comfort.   Improved thermal efficiency of ventilation radiators allows a lower supply water temperature and energy savings both for heating up and distribution of warm water in heat pumps or district heating systems. A secondary benefit is that a high ventilation rate can be maintained all year around without risk for cold draught.

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.