To eliminate the fluxes in the multipoint approximation, a block diagonal operator grad is constructed. The current implementation divides row and column permutation matrices into two linear applications, rows2blk_diag and cols2blk_diag, such that grad = rows2blk_diag @ grad_star @ cols2blk_diag, where grad_star is an operator being permuted. Performing the multipoint flux approximation with a direct construction of operator grad and possibly eliminating other global arrays will save computational memory and CPU time. This refactoring implies a deep modification of the files mpsa.py and mpfa.py. This issue reports that such an algebraic simplification may be necessary, particularly for larger problems.
To eliminate the fluxes in the multipoint approximation, a block diagonal operator
gradis constructed. The current implementation divides row and column permutation matrices into two linear applications, rows2blk_diag and cols2blk_diag, such thatgrad = rows2blk_diag @ grad_star @ cols2blk_diag, wheregrad_staris an operator being permuted. Performing the multipoint flux approximation with a direct construction of operator grad and possibly eliminating other global arrays will save computational memory and CPU time. This refactoring implies a deep modification of the files mpsa.py and mpfa.py. This issue reports that such an algebraic simplification may be necessary, particularly for larger problems.