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The experimental work demonstrates that even for larger problems the results of our method significantly surpass those of the related work with respect to both solution quality and the CPU time to obtain a solution.
When assessed on four well-known combinatorial optimization problems, our automatically-generated neighborhoods outperform state-of-the-art model-based neighborhoods with respect to both scalability and solution quality.
Experimental evidence shows that the proposed approach outperforms the competing methods with respect to both computing time and solution quality.
Comparisons between the approximate solution for two modes are made with respect to both one mode and numerical solutions.
The main idea behind adding a regularization term is to smoothen the problem with respect to both the functional and the solution so that well-posedness is assured and efficient computational methods can be defined to determine minimizers.
The main idea behind adding a regularization term is to smoothen the problem with respect to both the functional and the solution so that well-posedness is assured and efficient computational methods can be developed to determine minimizers.
Validations are conducted with respect to both the analytic viscoacoustic and viscoelastic solutions.
Therefore, answering online selective graph queries with a combination of light-weight indices and fast graph exploration makes it a feasible solution with respect to both time and space efficiency.
A similar solution with respect to both space and time can be developed following the transformation described by Uchida and Aoki [4], Dauenhauer and Majdalani [10], and Si et al. [20], respectively.
Comparison of this mutant structure to the X-ray structure of wild-type HgD and its NMR solution parameters, with respect to both structural details and conformational and dynamic properties, reveals a crucial difference in the atomic details surrounding the site of mutation, as well as changes in dynamic behavior.
Fast sensitivity analysis and very efficient computational cost are the benefit of the derived analytical solutions with respect to previous numerical solutions.
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