subgrid

we analyzed the data using a subgrid approach to reduce complexity.

the algorithm efficiently searches within a smaller subgrid.

a subgrid of the original image was selected for detailed analysis.

the simulation was run on a smaller subgrid to save computational time.

we defined a subgrid for each region of interest in the map.

the results were aggregated from multiple overlapping subgrids.

the model's performance was evaluated on a test subgrid.

we created a subgrid to represent the local neighborhood of each node.

the data was partitioned into several non-overlapping subgrids.

the system uses a hierarchical subgrid structure for efficient storage.

we compared the performance across different subgrid sizes.