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Up-> cppad_swig library sparse sparse_rcv

cppad_swig-> testing swig_xam.i library whats_new_2018

library-> module a_double vector a_fun sparse error xam.m4

sparse-> sparse_rc sparse_rcv jac_sparsity hes_sparsity sparse_jac sparse_hes

sparse_rcv-> sparse_rcv_xam.cpp sparse_rcv_xam.m sparse_rcv_xam.pm sparse_rcv_xam.py

Headings-> Syntax module_ref pattern matrix nr nc nnz put ---..k ---..v row col val row_major col_major Example

@(@\newcommand{\B}[1]{ {\bf #1} } \newcommand{\R}[1]{ {\rm #1} }@)@ Sparse Matrices
Syntax
matrix = module_ref sparse_rcv(pattern)
nr = matrix.nr()
nc = matrix.nc()
nnz = matrix.nnz()
matrix.put(k, v)
row = matrix.row()
col = matrix.col()
val = matrix.val()
row_major = matrix.row_major()
col_major = matrix.col_major()

module_ref
This is the module reference for the particular language.

pattern
This argument has prototype
     const sparse_rc& pattern
It specifies the number of rows, number of columns and the possibly non-zero entries in the matrix .

matrix
This is a sparse matrix object with the sparsity specified by pattern . Only the val vector can be changed. All other values returned by matrix are fixed during the constructor and constant there after. The val vector is only changed by the constructor and the set function.

nr
This return value has prototype
     int nr
and is the number of rows in the matrix.

nc
This return value has prototype
     int nc
and is the number of columns in the matrix.

nnz
This return value has prototype
     int nnz
and is the number of possibly non-zero values in the matrix.

put
This function sets the value
     val[k] = v
(The name set is used by Cppad, but not used here, because set it is a built-in name in Python.)

k
This argument has type
     int k
and must be less than nnz .

v
This argument has type
     double v
It specifies the value assigned to val[k] .

row
This result has type
     vec_int row
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.

col
This result has type
     vec_int col
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.

val
This result has type
     vec_double val
and its size is nnz . For k = 0, ..., nnz-1 , val[k] is the value of the k-th possibly non-zero entry in the matrix (the value may be zero).

row_major
This vector has prototype
     vec_int row_major
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] ]
and if col[ row_major[k] ] == col[ row_major[k+1] ] ,
     row[ row_major[k] ] < row[ row_major[k+1] ]
This routine generates an assert if there are two entries with the same row and column values (if NDEBUG is not defined).

col_major
This vector has prototype
     vec_int col_major
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] ]
and if row[ col_major[k] ] == row[ col_major[k+1] ] ,
     col[ col_major[k] ] < col[ col_major[k+1] ]
This routine generates an assert if there are two entries with the same row and column values (if NDEBUG is not defined).

Example
C++ , Octave , Perl , Python .

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Input File: lib/sparse.cpp