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pattern = cppad_py.sparse_rc()
pattern.resize(nr, nc, nnz)
nr = pattern.nr()
nc = pattern.nc()
nnz = pattern.nnz()
pattern.put(k, r, c)
row = pattern.row()
col = pattern.col()
row_major = pattern.row_major()
col_major = pattern.col_major()
resize and put operations.
int
and is the number of rows in the sparsity pattern.
The function nr() returns the value of
nr
in the previous resize operation.
int
and is the number of columns in the sparsity pattern.
The function nc() returns the value of
nc
in the previous resize operation.
int
and is the number of possibly non-zero
index pairs in the sparsity pattern.
The function nnz() returns the value of
nnz
in the previous resize operation.
resize, the elements in the
row
and
col
vectors should be assigned using put.
row[k] = r
col[k] = c
set is used by Cppad, but not used here,
because set it is a built-in name in Python.)
int
and must be less than
nnz
.
int
and must be less than
nr
.
It specifies the value assigned to
row[k]
.
int
and must be less than
nc
.
It specifies the value assigned to
col[k]
.
int elements
and its size is
nnz
.
For
k = 0, ..., nnz-1
,
row[k]
is the row index for the k-th possibly non-zero
entry in the matrix.
int elements
and its size is
nnz
.
For
k = 0, ..., nnz-1
,
col[k]
is the column index for the k-th possibly non-zero
entry in the matrix.
int elements
and its size
nnz
.
It sorts the sparsity pattern in row-major order.
To be specific,
col[ row_major[k] ] <= col[ row_major[k+1] ]
col[ row_major[k] ] == col[ row_major[k+1] ]
,
row[ row_major[k] ] < row[ row_major[k+1] ]
NDEBUG is not defined).
int elements
and its size
nnz
.
It sorts the sparsity pattern in column-major order.
To be specific,
row[ col_major[k] ] <= row[ col_major[k+1] ]
row[ col_major[k] ] == row[ col_major[k+1] ]
,
col[ col_major[k] ] < col[ col_major[k+1] ]
NDEBUG is not defined).