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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Averaged Iterative Water-Filling Algorithm: Robustness And Convergence, Mingyi Hong, Alfredo Garcia
Averaged Iterative Water-Filling Algorithm: Robustness And Convergence, Mingyi Hong, Alfredo Garcia
Mingyi Hong
The convergence properties of the iterative water-filling (IWF) based algorithms have been derived in the ideal situation where the transmitters in the network are able to obtain the exact value of the interference plus noise (IPN) experienced at the corresponding receivers in each iteration of the algorithm. However, these algorithms are not robust because they diverge when there is time-varying estimation error of the IPN, a situation that arises in real communication system. In this correspondence, we propose an algorithm that possesses convergence guarantees in the presence of various forms of such time-varying error. Moreover, we also show by simulation …
Kernel Regression In The Presence Of Correlated Errors, Kris De Brabanter, Jos De Brabanter, Johan A.K. Suykens, Bart De Moor
Kernel Regression In The Presence Of Correlated Errors, Kris De Brabanter, Jos De Brabanter, Johan A.K. Suykens, Bart De Moor
Kris De Brabanter
It is a well-known problem that obtaining a correct bandwidth and/or smoothing parameter in nonparametric regression is difficult in the presence of correlated errors. There exist a wide variety of methods coping with this problem, but they all critically depend on a tuning procedure which requires accurate information about the correlation structure. We propose a bandwidth selection procedure based on bimodal kernels which successfully removes the correlation without requiring any prior knowledge about its structure and its parameters. Further, we show that the form of the kernel is very important when errors are correlated which is in contrast to the …
Understanding Student Pathways In Context-Rich Problems, Pavlo Antonenko, John Jackman, Piyamart Kumsaikaew, Rahul Marathe, Dale Niederhauser, Craig Ogilvie, Sarah Ryan
Understanding Student Pathways In Context-Rich Problems, Pavlo Antonenko, John Jackman, Piyamart Kumsaikaew, Rahul Marathe, Dale Niederhauser, Craig Ogilvie, Sarah Ryan
Sarah M. Ryan
In this paper we investigate the ways that students' problem-solving behaviors evolve when solving multi-faceted, context-rich problems within a structured, computer-based learning environment. During the semester, groups of two or three students worked on several problems that required drawing on more than one concept and, hence, could not be readily solved with simple "plug-and-chug" strategies. The problems were presented to students in a data-rich, online problem-solving environment that tracked which information items were selected by students as they attempted to solve the problem. The students also completed a variety of tasks, like entering an initial qualitative analysis into an online …