Armadillo’s SPARSE matrix representation is advantageous in situations where the majority of elements are zero, as it only stores non-zero values, reducing memory usage and computational complexity. This is beneficial for large-scale problems with sparse data structures, such as graph analysis or natural language processing tasks.
On the other hand, DENSE matrix representation performs better when most elements are non-zero, as it stores all values in a contiguous block of memory. This allows for efficient cache utilization and vectorized operations, leading to faster computations. Dense matrices are suitable for tasks like image processing or machine learning algorithms that require dense data structures.
