| | DEFUN (sparse, args,,"-*- texinfo -*-\n\
@deftypefn {Loadable Function} {@var{s} =} sparse (@var{a})\n\
@deftypefnx {Loadable Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv}, @var{m}, @var{n}, @var{nzmax})\n\
@deftypefnx {Loadable Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv})\n\
@deftypefnx {Loadable Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{s}, @var{m}, @var{n}, \"unique\")\n\
@deftypefnx {Loadable Function} {@var{s} =} sparse (@var{m}, @var{n})\n\
Create a sparse matrix from the full matrix or row, column, value triplets.\n\
If @var{a} is a full matrix, convert it to a sparse matrix representation,\n\
removing all zero values in the process.\n\
\n\
Given the integer index vectors @var{i} and @var{j}, a 1-by-@code{nnz} vector\n\
of real of complex values @var{sv}, overall dimensions @var{m} and @var{n}\n\
of the sparse matrix. The argument @code{nzmax} is ignored but accepted for\n\
compatibility with @sc{matlab}. If @var{m} or @var{n} are not specified\n\
their values are derived from the maximum index in the vectors @var{i} and\n\
@var{j} as given by @code{@var{m} = max (@var{i})},\n\
@code{@var{n} = max (@var{j})}.\n\
\n\
@strong{Note}: if multiple values are specified with the same\n\
@var{i}, @var{j} indices, the corresponding values in @var{s} will\n\
be added.\n\
\n\
The following are all equivalent:\n\
\n\
@example\n\
@group\n\
s = sparse (i, j, s, m, n)\n\
s = sparse (i, j, s, m, n, \"summation\")\n\
s = sparse (i, j, s, m, n, \"sum\")\n\
@end group\n\
@end example\n\
\n\
Given the option \"unique\". if more than two values are specified for the\n\
same @var{i}, @var{j} indices, the last specified value will be used.\n\
\n\
@code{sparse(@var{m}, @var{n})} is equivalent to\n\
@code{sparse ([], [], [], @var{m}, @var{n}, 0)}\n\
\n\
If any of @var{sv}, @var{i} or @var{j} are scalars, they are expanded\n\
to have a common size.\n\
@seealso{full}\n\
@end deftypefn") |
| | DEFUN (spalloc, args,,"-*- texinfo -*-\n\
@deftypefn {Loadable Function} {@var{s} =} spalloc (@var{m}, @var{n}, @var{nz})\n\
Create an @var{m}-by-@var{n} sparse matrix with pre-allocated space for at\n\
most @var{nz} nonzero elements. This is useful for building the matrix\n\
incrementally by a sequence of indexed assignments. Subsequent indexed\n\
assignments will reuse the pre-allocated memory, provided they are of one of\n\
the simple forms\n\
\n\
@itemize\n\
@item @code{@var{s}(I:J) = @var{x}}\n\
\n\
@item @code{@var{s}(:,I:J) = @var{x}}\n\
\n\
@item @code{@var{s}(K:L,I:J) = @var{x}}\n\
@end itemize\n\
\n\
@b{and} that the following conditions are met:\n\
\n\
@itemize\n\
@item the assignment does not decrease nnz(@var{S}).\n\
\n\
@item after the assignment, nnz(@var{S}) does not exceed @var{nz}.\n\
\n\
@item no index is out of bounds.\n\
@end itemize\n\
\n\
Partial movement of data may still occur, but in general the assignment will\n\
be more memory and time-efficient under these circumstances. In particular,\n\
it is possible to efficiently build a pre-allocated sparse matrix from\n\
contiguous block of columns.\n\
\n\
The amount of pre-allocated memory for a given matrix may be queried using\n\
the function @code{nzmax}.\n\
@seealso{nzmax, sparse}\n\
@end deftypefn") |
