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Simulation models typically describe complicated systems with no closed-form analytic expression. To optimize these complex models, general “black-box” optimization techniques must be used. To confront computational limitations, Optimal Computational Budget Allocation (OCBA) algorithms have been developed in order to arrive at the best solution relative to a finite amount of resources primarily for...
BDM-NBI algorithm is proposed at this paper. It focuses on the analysis of a personalized recommendation algorithm that utilizes a weighted bipartite graph suitable for processing big data. Our algorithm adopts bipartite graph partitioning using a vertex separator method that partitions a high-dimensional sparse matrix into a pseudo-block based diagonal matrix. Then, the recommendation algorithm analyzes...
Software-Defined Networking (SDN) enables flexible network resource allocations for traffic engineering, but at the same time the scalability problem becomes more serious since traffic is more difficult to be aggregated. Those crucial issues in SDN have been studied for unicast but have not been explored for multicast traffic, and addressing those issues for multicast is more challenging since the...
Spectral Embedding is one of the most effective dimension reduction algorithms in data mining. However, its computation complexity has to be mitigated in order to apply it for real-world large scale data analysis. Many researches have been focusing on developing approximate spectral embeddings which are more efficient, but meanwhile far less effective. This paper proposes Diverse Power Iteration Embeddings...
We present a simulation optimization algorithm called probabilistic branch and bound with confidence intervals (PBnB with CI), which is designed to approximate a level set of solutions for a user-defined quantile. PBnB with CI is developed for both deterministic and noisy problems with mixed continuous and discrete variables. The quality of the results is statistically analyzed with order statistic...
In this paper, we propose a new approach for image deblurring from two images, non-blurred and blurred, in different poses by exploiting the co-existing planar object in both views. We focus on the problem of aligning the corresponding image patches, which are the co-existing planar object, in both images and propose an iterative two-stage algorithm for patch alignment and kernel estimation. In the...
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