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From 3.3, 3.4 and 3.5, it is clear that points of the form E ( x ¯ ) + λ d are feasible to the problem P E for each λ ∈ ( 0, δ ), where δ = min ( δ 1, δ 2, δ 3 ).
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The solution obtained is called Smart Greedy solution, which is feasible to the original problem.
Furthermore, since the solution of the integer subproblem is feasible to the original problem an upper bound is obtained immediately.
The results show that our iterative approach is feasible to tackle the problem of improving the POGO data quality by altering the satellite orbit.
For small networks or for a small number of resources, it is feasible to solve the problem by checking all the possibilities of locating the resources and choosing the optimal location.
The size is chosen so that an individual region captures fine enough information about the specific component in it, and the number of regions is chosen so that most of the area of the cell is covered while it is computationally feasible to solve the problem.
We expect that until multiple samples from each individual tumor can be obtained in a widespread manner, it will not be feasible to address the problem of estimating multiple cancer profiles for each tumor sample because of the small sample sizes.
It is shown that the weights algorithm is feasible to the linear bilevel multiobjective programming problem.
An algorithm is first developed to find all feasible solutions to the problem.
Living donor liver transplantation (LDLT) using left-lobe grafts was not generally recognized as feasible due to the problem of graft size.
Upon termination, the resulting assignment matrix X (n)∈[0,1] M×N needs to be mapped to a matrix X ⋆∈{0,1} M×N in order to obtain a feasible point to the problem in (5).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com