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Up-> cppad_py library cpp_lib sparse cpp_sparse_rc

cppad_py-> setup.py library whats_new_2018

library-> py_lib cpp_lib

cpp_lib-> a_double vector cpp_fun sparse cpp_utility more_cpp

sparse-> cpp_sparse_rc cpp_sparse_rcv cpp_jac_sparsity cpp_sparsity cpp_sparse_jac cpp_sparse_hes

cpp_sparse_rc-> sparse_rc_xam.cpp

Headings-> Syntax pattern nr nc nnz resize put ---..k ---..r ---..c row col row_major col_major Example

@(@\newcommand{\B}[1]{ {\bf #1} } \newcommand{\R}[1]{ {\rm #1} }@)@Sparsity Patterns
Syntax
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()

pattern
This result has prototype
     sparse_rc pattern
It is used to hold a sparsity pattern for a matrix. The sparsity pattern is const except during the resize and put operations.

nr
This argument has prototype
     int nr
is non-negative, and specifies the number of rows in the sparsity pattern. The function nr() returns the value of nr in the previous resize operation.

nc
This argument has prototype
     int nc
is non-negative, and specifies the number of columns in the sparsity pattern. The function nc() returns the value of nc in the previous resize operation.

nnz
This argument and result has prototype
     int nnz
It 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 current sparsity pattern is lost and a new one is started with the specified parameters. After each resize, the elements in the row and col vectors should be assigned using put.

put
This function sets the values
     row[k] = r
     col[k] = c
(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
is non-negative, and must be less than nnz .

r
This argument has type
     int r
is non-negative, and must be less than nr . It specifies the value assigned to row[k] and must

c
This argument has type
     int c
It specifies the value assigned to col[k] and must be less than nc .

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.

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
sparse_rc_xam.cpp

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Input File: lib/cplusplus/sparse.cpp