Hence, 30 min is considered to be optimal time to get monodispersed AuNPs.
However, it is difficult to get the optimal solution of data association across cameras.
Namely, it is difficult to get the optimal solution from (26).
In large environments, it is difficult to get global optimal of the map and the camera poses due to the local minimum problem in BA.
However, a general observation follows with the exact approach, which is: as the number of rectangles to be packed grow in number, it is difficult to get the optimal solution in an acceptable time as the time also grows exponentially.
GM-based tracking To track humans through partite graph matching based on input observations (detections, tracklets, trajectories, or pairs) MAP optimization solution framework Human tracking in complex scenes such as occlusion, crowd, and interference of appearance similarity It is difficult to get the optimal solution.
It is even possible to get close to the optimal efficiency of CSMA/CA without any knowledge of the number of contenders.
As for (mathcal {L}_{PHY}), it is extremely difficult to get closed-form optimal analytical solutions to (phantom {dot {i}!}mathbf {P}_{mathbf {S}_{1}}), (phantom {dot {i}!}mathbf {P}_{mathbf {S}_{2}}), and P R. In addition, the computation complexity would be unacceptable for implementation.
The interpolation for parametric curves is essentially an optimization problem, and it is extremely difficult to get the time-optimal solution.
Therefore, it is very difficult to get the global optimal solution, especially when the censoring is heavy.
