Armadillo’s memory management utilizes a combination of lazy evaluation and copy-on-write techniques to optimize matrix operations. Lazy evaluation defers computation until absolutely necessary, reducing redundant calculations. Copy-on-write creates shallow copies of matrices, only duplicating data when modifications occur.
To efficiently use memory allocations in Armadillo:
1. Use submatrix views (e.g., .submat()) instead of extracting submatrices, avoiding unnecessary copying.
2. Utilize inplace operations (e.g., .transform()) to modify matrices without creating new instances.
3. Employ element-wise operations for faster execution and reduced memory usage.
4. Take advantage of Armadillo’s built-in functions (e.g., .zeros(), .eye()) for common matrix initializations.
5. Opt for expressions like A *= B instead of A = A * B to reduce temporary object creation.
6. When possible, use sparse matrices to save memory on large matrices with many zeros.
