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This solution is optimal when it satisfies constraint (29b), otherwise it is suboptimal.
If there exists a partition (satisfying the capacity and the distance constraints) corresponding to Y∗(b), then this solution is optimal to the problems (2), (3), and (4).
So this solution is optimal in terms of the number of bad rows, and in the bad row there is a solution with only one bad zero, then optimality is guaranteed also for the number of bad zeros.
Similar(57)
This initial solution is optimal (achieves minimum square error) once the grid size and the shift increment are fixed.
Consequently, the estimate (4.34) of the corresponding numerical solution is optimal in this sense.
This solver cannot determine whether a given solution is optimal, nevertheless good solutions can be attained under heuristic rules or other predefined stopping criteria.
Whereas in horizontal environments a "travelling salesman" (distance-minimising) solution is optimal, when there is a vertical component this solution should no longer be preferred.
Therefore, to guarantee that a given feasible solution is optimal, the solution should be compared with any other feasible solution.
Our solution is optimal, but does not scale well with the problem size.
The achieved solution is optimal with regard to the model of the system.
When (eta =0), an ((varepsilon,eta))-optimal solution is optimal over all ε-feasible solutions of problem (P).
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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