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.

Investigation of the drying process of linseed oil using FTIR and ToF-SIMS

The drying process of linseed oil, oxidized at 80 oC, has been investigated with rheology measurements, Fourier transformation infrared spectroscopy (FTIR), and time of flight secondary ion mass spectrometry (ToF-SIMS). The drying process can be divided into three main steps: initiation, propagation and termination. ToF-SIMS spectra show that the oxidation is initiated at the linolenic (three double bonds) and linoleic fatty acids (two double bonds). ToF-SIMS spectra reveal peaks that can be assigned to ketones, alcohols and hydroperoxides. In this article it is shown that FTIR in combination with ToF-SIMS are well suited tools for investigations of various fatty acid components and reaction products of linseed oil.