In this article the results of the application of two numerical approaches - the Method of Moments and the nodal method - for the prediction of the evaporation phenomena in the synthesis of nanoparticles are presented and compared, in order to evaluate the limits of the moment methods and to determine the usability of the method in plasma environments (i.e. high temperatures and steep gradients). Furthermore, a new closure term is introduced in the Method of Moments, in order to consider the disappearance of the particles due to the evaporation process. The Nodal Method is used as a benchmark for the Method of Moments.
In recent years, GPU computing has become an increasingly important tool to develop efficient applications in several areas, including optimization. One of the optimization approaches that seems to take most advantage from GPU computing is dynamic programming. In this paper, we investigate the application of GPU computing to the two-dimensional guillotine cutting problem, solved by dynamic programming. We show a possible implementation and we discuss a number of technical issues. Computational results on test instances available in the literature and on new larger instances show the effectiveness of the dynamic programming approach based on GPU computing for this problem. Data, as supplemental material, are available at http://dx.doi.org/10.1287/ijoc.2016.0693 .
Mobile platforms have matured to a point where they can provide the infrastructure required to support sophisticated optimization codes. This opens the possibility to envisage new interest for distributed application codes and the opportunity to intensify research on optimization algorithms requiring limited computational resources, as provided by mobile platforms.
In this article, we report on some exploratory experience in this area. We illustrate some practical, real-world cases where running optimization programs on mobile or embedded devices can be useful, with particular emphasis on matheuristics approaches. Then, we discuss a practical use case involving the feasibility version of the generalized assignment problem (GAP). We present a JavaScript implementation of a GAP solver that can be executed inside an ordinary browser supporting ECMAScript. We tested the code on different smartphones of varying age and power, as well as on desktop PCs and other embedded devices. Our experiments confirm the viability of mobile devices for computational intensive tasks.
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