Armadillo’s submatrix views provide efficient access to contiguous subsets of matrices without copying data. They enable operations on specific matrix regions, improving memory management and computational efficiency.
Submatrix views are created using the “submat” function with row and column indices specifying the region. For example, A.submat(r1, c1, r2, c2) creates a view from rows r1 to r2 and columns c1 to c2 in matrix A.
Benefits include:
1. Memory Management: Submatrix views don’t allocate new memory or copy data; they reference existing elements, reducing memory usage.
2. Computational Efficiency: Operations on submatrices avoid unnecessary computations on irrelevant elements, speeding up calculations.
Armadillo also supports advanced submatrix views like non-contiguous submatrices (using “submat(span(), span())”) and diagonal views (“diagmat()”).
