Temporal Decomposition
A network architecture design approach that exploits the natural block structure of NMPC problems along the time dimension. By decomposing the solver architecture according to time steps, it decouples the network size from the prediction horizon length, thereby improving solver scalability.
Timeline (last 90 days)
This study redesigns the solver architecture by exploiting the temporal structure of NMPC problems, so that the network size no longer depends on the length of the prediction horizon.
The temporal decomposition modification of the new architecture does not change the training loss function or the iterative logic of the solver, and is implemented solely at the architectural level.
The new solver can handle problems of larger scale than monolithic LISCO, demonstrating that temporal decomposition removes the horizon bottleneck.
All Facts (3)
This study redesigns the solver architecture by exploiting the temporal structure of NMPC problems, so that the network size no longer depends on the length of the prediction horizon.
50%UnverifiedThe temporal decomposition modification of the new architecture does not change the training loss function or the iterative logic of the solver, and is implemented solely at the architectural level.
50%UnverifiedThe new solver can handle problems of larger scale than monolithic LISCO, demonstrating that temporal decomposition removes the horizon bottleneck.
50%