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It is obvious that the above mentioned objective functions are in conflict with each other; and optimization according to a certain objective function in a certain time, leads to deviation from optimal solutions of other objective functions.
For each instance we compared the methods performance in terms of reaching optimality, deviation from the optimal solution and calculation-time.
The best heuristics can yield a near-optimal solution with an average 0.8% deviation from the optimal solution within a reasonable time.
To gain insight into the deviation from the optimal solution of (31 to 33), we compare our algorithm's performance in the simplified setting with a (near) optimal solution based on BNB.
When deviations are found, the difference from the optimal solution is minimal (cf. Additional file 1: Figure S1).
Using Lagrangean relaxation and subgradient optimization techniques, we obtain high-quality solutions with information about their deviations from true optimal solutions.
Figure 22 Deviation from optimal delta for equivalent synthetic datasets.
We observed that even with some deviation from optimal design, the LOS MIMO case outperforms the theoretical i.i.d.i.d
This average deviation from optimal performance (error) was also normalized to baseline performance.
Replacement should occur if the cause of closures is errors, that is, deviation from optimal quality.
However, deviation from optimal alignment persists in some cases, especially in the transverse plane (rotational alignment) [ 14- 17].
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