callable functions

    function snls_initialize(T, INT, data, control, inform)

Set default control values and initialize private data

Parameters:

data	holds private internal data
control	is a structure containing control information (see snls_control_type)
inform	is a structure containing output information (see snls_inform_type)

    function snls_read_specfile(T, INT, control, specfile)

Read the content of a specification file, and assign values associated with given keywords to the corresponding control parameters. An in-depth discussion of specification files is available, and a detailed list of keywords with associated default values is provided in $GALAHAD/src/snls/SNLS.template. See also Table 2.1 in the Fortran documentation provided in $GALAHAD/doc/snls.pdf for a list of how these keywords relate to the components of the control structure.

Parameters:

control	is a structure containing control information (see snls_control_type)
specfile	is a one-dimensional array of type Vararg{Cchar} that must give the name of the specification file

    function snls_import(T, INT, control, data, status, n, m_r, m_c,
                         Jr_type, Jr_ne, Jr_row, Jr_col, Jr_ptr_ne, Jr_ptr,
                         cohort)

Import problem data into internal storage prior to solution.

Parameters:

control	is a structure whose members provide control parameters for the remaining procedures (see snls_control_type)
data	holds private internal data
status	is a scalar variable of type INT that gives the exit status from the package. Possible values are: 1 The import was successful, and the package is ready for the solve phase -1 An allocation error occurred. A message indicating the offending array is written on unit control.error, and the returned allocation status and a string containing the name of the offending array are held in inform.alloc_status and inform.bad_alloc respectively. -2 A deallocation error occurred. A message indicating the offending array is written on unit control.error and the returned allocation status and a string containing the name of the offending array are held in inform.alloc_status and inform.bad_alloc respectively. -3 The restrictions n > 0, m > 0 or requirement that Jr_type contains its relevant string ‘dense’, ‘dense_by_rows’, ‘dense_by_columns’, ‘coordinate’, ‘sparse_by_rows’ or ‘sparse_by_columns’ has been violated.
n	is a scalar variable of type INT that holds the number of variables.
m_r	is a scalar variable of type INT that holds the number of residuals.
m_c	is a scalar variable of type INT that holds the number of cohorts.
Jr_type	is a one-dimensional array of type Vararg{Cchar} that specifies the unsymmetric storage scheme used for the Jacobian, \(J_r\). It should be one of ‘coordinate’, ‘sparse_by_rows’, ‘dense’ or ‘absent’, the latter if access to the Jacobian is via matrix-vector products; lower or upper case variants are allowed.
Jr_ne	is a scalar variable of type INT that holds the number of entries in \(J_r\) in the sparse co-ordinate storage scheme. It need not be set for any of the other schemes.
Jr_row	is a one-dimensional array of size Jr_ne and type INT that holds the row indices of \(J_r\) in the sparse co-ordinate storage scheme. It need not be set for any of the other schemes, and in this case can be C_NULL.
Jr_col	is a one-dimensional array of size Jr_ne and type INT that holds the column indices of \(J_r\) in either the sparse co-ordinate, or the sparse row-wise storage scheme. It need not be set when the dense or diagonal storage schemes are used, and in this case can be C_NULL.
J_ptr_ne	is a scalar variable of type INT, that holds the length of the pointer array if sparse row or column storage scheme is used for \(J_r\). For the sparse row scheme, Jr_ptr_ne should be at least m_r+1, while for the sparse column scheme, it should be at least n+1, It should be set to 0 when the other schemes are used.
Jr_ptr	is a one-dimensional array of size m+1 and type INT that holds the starting position of each row of \(J_r\), as well as the total number of entries, in the sparse row-wise storage scheme. It need not be set when the other schemes are used, and in this case can be C_NULL.
cohort	is a one-dimensional array of size m and type INT that specifies which cohort each variable is assigned to. If variable \(x_j\) is associated with cohort \(\cal C_i\), \(1 \leq i \leq m_c\), cohort[j] should be set to i, while if \(x_j\) is unconstrained cohort[j] = 0 should be assigned. At least one value cohort[j] for \(j = 1,\ldots\,n\) is expected to take the value \(i\) for every \(1 \leq i \leq m_c\), that is no empty cohorts are allowed. If all the variables lie in a single simplex, cohort can be set to C_NULL.

    function snls_import_without_jac(T, INT, control, data, status,
                                     n, m_r, m_c, cohort)

Import problem data, excluding the structure of \(J_r(x)\), into internal storage prior to solution.

Parameters:

control	is a structure whose members provide control parameters for the remaining procedures (see snls_control_type)
data	holds private internal data
status	is a scalar variable of type INT that gives the exit status from the package. Possible values are: 1 The import was successful, and the package is ready for the solve phase -1 An allocation error occurred. A message indicating the offending array is written on unit control.error, and the returned allocation status and a string containing the name of the offending array are held in inform.alloc_status and inform.bad_alloc respectively. -2 A deallocation error occurred. A message indicating the offending array is written on unit control.error and the returned allocation status and a string containing the name of the offending array are held in inform.alloc_status and inform.bad_alloc respectively. -3 The restrictions n > 0, m > 0 has been violated.
n	is a scalar variable of type INT that holds the number of variables.
m_r	is a scalar variable of type INT that holds the number of residuals.
m_c	is a scalar variable of type INT that holds the number of cohorts.
cohort	is a one-dimensional array of size m and type INT that specifies which cohort each variable is assigned to. If variable \(x_j\) is associated with cohort \(\cal C_i\), \(1 \leq i \leq m_c\), cohort[j] should be set to i, while if \(x_j\) is unconstrained cohort[j] = 0 should be assigned. At least one value cohort[j] for \(j = 1,\ldots\,n\) is expected to take the value \(i\) for every \(1 \leq i \leq m_c\), that is no empty cohorts are allowed. If all the variables lie in a single simplex, cohort can be set to C_NULL.

    function snls_reset_control(T, INT, control, data, status)

Reset control parameters after import if required.

Parameters:

control	is a structure whose members provide control parameters for the remaining procedures (see snls_control_type)
data	holds private internal data
status	is a scalar variable of type INT that gives the exit status from the package. Possible values are: 1 The import was successful, and the package is ready for the solve phase

    function snls_solve_with_jac(T, INT, data, userdata, status,
                                 n, m_r, m_c, x, y, z, r, g, x_stat,
                                 eval_r, Jr_ne, eval_jr, w)

Solve the simplex-constrained nonlinear least-squares problem when the Jacobian \(J_r(x)\) is available by function calls.

Parameters:

data	holds private internal data
userdata	is a structure that allows data to be passed into the function and derivative evaluation programs.
status	is a scalar variable of type INT that gives the entry and exit status from the package. On initial entry, status must be set to 1. Possible exit values are: 0 The run was successful -1 An allocation error occurred. A message indicating the offending array is written on unit control.error, and the returned allocation status and a string containing the name of the offending array are held in inform.alloc_status and inform.bad_alloc respectively. -2 A deallocation error occurred. A message indicating the offending array is written on unit control.error and the returned allocation status and a string containing the name of the offending array are held in inform.alloc_status and inform.bad_alloc respectively. -3 The restriction n > 0 or requirement that type contains its relevant string ‘dense’, ‘coordinate’, ‘sparse_by_rows’, ‘diagonal’ or ‘absent’ has been violated. -9 The analysis phase of the factorization failed; the return status from the factorization package is given in the component inform.factor_status -10 The factorization failed; the return status from the factorization package is given in the component inform.factor_status. -11 The solution of a set of linear equations using factors from the factorization package failed; the return status from the factorization package is given in the component inform.factor_status. -16 The problem is so ill-conditioned that further progress is impossible. -17 The step is too small to make further impact. -18 Too many iterations have been performed. This may happen if control.maxit is too small, but may also be symptomatic of a badly scaled problem. -19 The CPU time limit has been reached. This may happen if control.cpu_time_limit is too small, but may also be symptomatic of a badly scaled problem. -82 The user has forced termination of solver by removing the file named control.alive_file from unit unit control.alive_unit.
n	is a scalar variable of type INT that holds the number of variables.
m_r	is a scalar variable of type INT that holds the number of residuals.
m_c	is a scalar variable of type INT that holds the number of cohorts.
x	is a one-dimensional array of size n and type T that holds the values \(x\) of the optimization variables. The j-th component of x, j = 1, … , n, contains \(x_j\).
y	is a one-dimensional array of size n and type T that holds the values \(y\) of the Lagrange multipliers. The i-th component of y, i = 1, … , m_c, contains \(y_i\).
z	is a one-dimensional array of size n and type T that holds the values \(z\) of the dual variables. The j-th component of z, j = 1, … , n, contains \(z_j\).
r	is a one-dimensional array of size m and type T that holds the residual \(r(x)\). The i-th component of r, i = 1, … , m_r, contains \(r_i(x)\).
g	is a one-dimensional array of size n and type T that holds the gradient \(g = \nabla_xf(x)\) of the objective function. The j-th component of g, j = 1, … , n, contains \(g_j\).
x_stat	is a one-dimensional array of size n and type INT that gives the optimal status of the problem variables. If x_stat[j] is negative, variable \(x_j\) most likely lies at its zero, lower bound, while if it is zero, \(x_j\) is free of its bound (or unconstrained).
eval_r	is a user-supplied function that must have the following signature: function eval_r(n, m_r, x, r, userdata) The componnts of the residual function \(r(x)\) evaluated at x=\(x\) must be assigned to r, and the function return value set to 0. If the evaluation is impossible at x, return should be set to a nonzero value. Data may be passed into eval_r via the structure userdata.
Jr_ne	is a scalar variable of type INT that holds the number of entries in the Jacobian matrix \(J_r\).
eval_jr	is a user-supplied function that must have the following signature: function eval_jr(n, m_r, jr_ne, x, jr_val, userdata) The components of the Jacobian \(J_r = \nabla_x r(x\)) of the residuals must be assigned to jr_val in the same order as presented to snls_import, and the function return value set to 0. If the evaluation is impossible at x, return should be set to a nonzero value. Data may be passed into eval_jr via the structure userdata.
w	is a one-dimensional array of size m_r and type T that holds the values \(w\) of the weights on the residuals in the least-squares objective function. It need not be set if the weights are all ones, and in this case can be C_NULL.

    function snls_solve_with_jacprod(T, INT, data, userdata, status,
                                     n, m_r, m_c, x, y, z, r, g, x_stat,
                                     eval_r, eval_jr_prod, eval_jr_scol,
                                     eval_jr_sprod, w)

Solve the simplex-constrained nonlinear least-squares problem when the products of the Jacobian \(J_r(x)\) and its transpose are available by function calls.

Parameters:

data	holds private internal data
userdata	is a structure that allows data to be passed into the function and derivative evaluation programs.
status	is a scalar variable of type INT that gives the entry and exit status from the package. On initial entry, status must be set to 1. Possible exit values are: 0 The run was successful -1 An allocation error occurred. A message indicating the offending array is written on unit control.error, and the returned allocation status and a string containing the name of the offending array are held in inform.alloc_status and inform.bad_alloc respectively. -2 A deallocation error occurred. A message indicating the offending array is written on unit control.error and the returned allocation status and a string containing the name of the offending array are held in inform.alloc_status and inform.bad_alloc respectively. -3 The restriction n > 0 or requirement that type contains its relevant string ‘dense’, ‘coordinate’, ‘sparse_by_rows’, ‘diagonal’ or ‘absent’ has been violated. -9 The analysis phase of the factorization failed; the return status from the factorization package is given in the component inform.factor_status -10 The factorization failed; the return status from the factorization package is given in the component inform.factor_status. -11 The solution of a set of linear equations using factors from the factorization package failed; the return status from the factorization package is given in the component inform.factor_status. -16 The problem is so ill-conditioned that further progress is impossible. -17 The step is too small to make further impact. -18 Too many iterations have been performed. This may happen if control.maxit is too small, but may also be symptomatic of a badly scaled problem. -19 The CPU time limit has been reached. This may happen if control.cpu_time_limit is too small, but may also be symptomatic of a badly scaled problem. -82 The user has forced termination of solver by removing the file named control.alive_file from unit unit control.alive_unit.
n	is a scalar variable of type INT that holds the number of variables
m_r	is a scalar variable of type INT that holds the number of residuals.
m_c	is a scalar variable of type INT that holds the number of cohorts.
x	is a one-dimensional array of size n and type T that holds the values \(x\) of the optimization variables. The j-th component of x, j = 1, … , n, contains \(x_j\).
y	is a one-dimensional array of size n and type T that holds the values \(y\) of the Lagrange multipliers. The i-th component of y, i = 1, … , m_c, contains \(y_i\).
z	is a one-dimensional array of size n and type T that holds the values \(z\) of the dual variables. The j-th component of z, j = 1, … , n, contains \(z_j\).
r	is a one-dimensional array of size m and type T that holds the residual \(r(x)\). The i-th component of r, i = 1, … , m_r, contains \(r_i(x)\).
g	is a one-dimensional array of size n and type T that holds the gradient \(g = \nabla_xf(x)\) of the objective function. The j-th component of g, j = 1, … , n, contains \(g_j\).
x_stat	is a one-dimensional array of size n and type INT that gives the optimal status of the problem variables. If x_stat[j] is negative, variable \(x_j\) most likely lies at its zero, lower bound, while if it is zero, \(x_j\) is free of its bound (or unconstrained).
eval_r	is a user-supplied function that must have the following signature: function eval_r(n, m_r, x, r, userdata) The componnts of the residual function \(r(x)\) evaluated at x=\(x\) must be assigned to r, and the function return value set to 0. If the evaluation is impossible at x, return should be set to a nonzero value. Data may be passed into eval_r via the structure userdata.
eval_jr_prod	is a user-supplied function that must have the following signature: function eval_jr_prod(n, m_r, x, transpose, v, p, got_jr, userdata) The product \(p = J_r(x) v\) (if the Bool transpose is false) or \(p = J_r^T(x) v\) (if tranpose is true) between the Jacobian \(J_r(x) \nabla_{x}r_(x)\), evaluated at x=\(x\), or its tranpose with the vector v=\(v\) must be returned in p, and the function return value set to 0. If the evaluation is impossible at x, return should be set to a nonzero value. Data may be passed into eval_jr_prod via the structure userdata.
eval_jr_scol	is a user-supplied function that must have the following signature: function eval_jr_scol(n, m_r, x, index, val, row, nz, got_jr, userdata) The nonzeros and corresponding row entries of the index-th colum of \(J_r(x)\) evaluated at x=\(x\) must be returned in val and row, respectively, together with the number of entries, nz, and the function return value set to 0. If the evaluation is impossible at x, return should be set to a nonzero value. Data may be passed into eval_jr_scol via the structure userdata.
eval_jr_sprod	is a user-supplied function that must have the following signature: function eval_jr_sprod(n, m_r, x, transpose, v, p, free, n_free, got_jr, userdata) The product \(J_r(x) v\) (if tranpose is false) or \(J_r^T(x) v\) (if tranpose is true) bewteen the Jacobian \(J_r(x) = \nabla_{x}r(x)\), evaluated at x=\(x\), or its tranpose with the vector v=\(v\) must be returned in p, and the function return value set to 0. If transpose is false, only the components free[1:n_free] of \(v\) will be nonzero, while if transpose is true, only the components free[1:n_free] of p should be set. If the evaluation is impossible at x, return should be set to a nonzero value. Data may be passed into eval_jr_sprod via the structure userdata
w	is a one-dimensional array of size m and type T that holds the values \(w\) of the weights on the residuals in the least-squares objective function. It need not be set if the weights are all ones, and in this case can be C_NULL.

    function snls_solve_reverse_with_jac(T, INT, data, status, eval_status,
                                        n, m_r, m_c, x, y, z, r, g, x_stat,
                                        jr_ne, Jr_val, w)

Solve the simplex-constrained nonlinear least-squares problem when the Jacobian \(J_r(x)\) may be computed by the calling program.

Parameters:

data	holds private internal data
status	is a scalar variable of type INT that gives the entry and exit status from the package. On initial entry, status must be set to 1. Possible exit values are: 0 The run was successful -1 An allocation error occurred. A message indicating the offending array is written on unit control.error, and the returned allocation status and a string containing the name of the offending array are held in inform.alloc_status and inform.bad_alloc respectively. -2 A deallocation error occurred. A message indicating the offending array is written on unit control.error and the returned allocation status and a string containing the name of the offending array are held in inform.alloc_status and inform.bad_alloc respectively. -3 The restriction n > 0 or requirement that type contains its relevant string ‘dense’, ‘coordinate’, ‘sparse_by_rows’, ‘diagonal’ or ‘absent’ has been violated. -9 The analysis phase of the factorization failed; the return status from the factorization package is given in the component inform.factor_status -10 The factorization failed; the return status from the factorization package is given in the component inform.factor_status. -11 The solution of a set of linear equations using factors from the factorization package failed; the return status from the factorization package is given in the component inform.factor_status. -16 The problem is so ill-conditioned that further progress is impossible. -17 The step is too small to make further impact. -18 Too many iterations have been performed. This may happen if control.maxit is too small, but may also be symptomatic of a badly scaled problem. -19 The CPU time limit has been reached. This may happen if control.cpu_time_limit is too small, but may also be symptomatic of a badly scaled problem. -82 The user has forced termination of solver by removing the file named control.alive_file from unit unit control.alive_unit. 2 The user should compute the vector of residuals \(r(x)\) at the point \(x\) indicated in x and then re-enter the function. The required value should be set in r, and eval_status should be set to 0. If the user is unable to evaluate \(r(x)\) for instance, if the function is undefined at \(x\) the user need not set r, but should then set eval_status to a non-zero value. 3 The user should compute the Jacobian of the vector of residual functions, \(J_r(x) = \nabla_x c(x)\), at the point \(x\) indicated in x and then re-enter the function. The l-th component of the Jacobian stored according to the scheme specified for the remainder of \(J_r\) in the earlier call to snls_import should be set in Jr_val[l], for l = 1, …, Jr_ne and eval_status should be set to 0. If the user is unable to evaluate a component of \(J_r\) for instance, if a component of the matrix is undefined at \(x\) the user need not set Jr_val, but should then set eval_status to a non-zero value.
eval_status	is a scalar variable of type INT that is used to indicate if objective function/gradient/Hessian values can be provided (see above)
n	is a scalar variable of type INT that holds the number of variables
m_r	is a scalar variable of type INT that holds the number of residuals.
m_c	is a scalar variable of type INT that holds the number of cohorts.
x	is a one-dimensional array of size n and type T that holds the values \(x\) of the optimization variables. The j-th component of x, j = 1, … , n, contains \(x_j\).
y	is a one-dimensional array of size n and type T that holds the values \(y\) of the Lagrange multipliers. The i-th component of y, i = 1, … , m_c, contains \(y_i\).
z	is a one-dimensional array of size n and type T that holds the values \(z\) of the dual variables. The j-th component of z, j = 1, … , n, contains \(z_j\).
r	is a one-dimensional array of size m and type T that holds the residual \(r(x)\). The i-th component of r, i = 1, … , m, contains \(r_i(x)\). See status = 2, above, for more details.
g	is a one-dimensional array of size n and type T that holds the gradient \(g = \nabla_xf(x)\) of the objective function. The j-th component of g, j = 1, … , n, contains \(g_j\).
x_stat	is a one-dimensional array of size n and type INT that gives the optimal status of the problem variables. If x_stat[j] is negative, variable \(x_j\) most likely lies at its zero, lower bound, while if it is zero, \(x_j\) is free of its bound (or unconstrained).
Jr_ne	is a scalar variable of type INT that holds the number of entries in the Jacobian matrix \(J_r\).
Jr_val	is a one-dimensional array of size Jr_ne and type T that holds the values of the entries of the Jacobian matrix \(J_r\) in any of the available storage schemes. See status = 3, above, for more details.
w	is a one-dimensional array of size m and type T that holds the values \(w\) of the weights on the residuals in the least-squares objective function. It need not be set if the weights are all ones, and in this case can be C_NULL.

    function snls_solve_reverse_with_jacprod(T, INT, data, status, eval_status,
                                             n, m_r, m_c, x, y, z, r, g, x_stat,
                                             v, iv, lvl, lvu, index, p, ip, lp, w)

Solve the simplex-constrained nonlinear least-squares problem when the products of the Jacobian \(J_r(x)\) and its transpose with specified vectors may be computed by the calling program.

Parameters:

data	holds private internal data
status	is a scalar variable of type INT that gives the entry and exit status from the package. On initial entry, status must be set to 1. Possible exit values are: 0 The run was successful -1 An allocation error occurred. A message indicating the offending array is written on unit control.error, and the returned allocation status and a string containing the name of the offending array are held in inform.alloc_status and inform.bad_alloc respectively. -2 A deallocation error occurred. A message indicating the offending array is written on unit control.error and the returned allocation status and a string containing the name of the offending array are held in inform.alloc_status and inform.bad_alloc respectively. -3 The restriction n > 0 or requirement that type contains its relevant string ‘dense’, ‘coordinate’, ‘sparse_by_rows’, ‘diagonal’ or ‘absent’ has been violated. -9 The analysis phase of the factorization failed; the return status from the factorization package is given in the component inform.factor_status -10 The factorization failed; the return status from the factorization package is given in the component inform.factor_status. -11 The solution of a set of linear equations using factors from the factorization package failed; the return status from the factorization package is given in the component inform.factor_status. -16 The problem is so ill-conditioned that further progress is impossible. -17 The step is too small to make further impact. -18 Too many iterations have been performed. This may happen if control.maxit is too small, but may also be symptomatic of a badly scaled problem. -19 The CPU time limit has been reached. This may happen if control.cpu_time_limit is too small, but may also be symptomatic of a badly scaled problem. -82 The user has forced termination of solver by removing the file named control.alive_file from unit unit control.alive_unit. 2 The user should compute the vector of residuals \(r(x)\) at the point \(x\) indicated in x and then re-enter the function. The required value should be set in r, and eval_status should be set to 0. If the user is unable to evaluate \(r(x)\) for instance, if the function is undefined at \(x\) the user need not set r, but should then set eval_status to a non-zero value. 4 The user should compute the product \(p = J_r(x) v\), at the point \(x\) indicated in x, between the product of the Jacobian \(J_r(x) = \nabla_{x}c_(x)\) with the vector v= \(v\), and then re-enter the function. The result should be set in p, and eval_status should be set to 0. If the user is unable to evaluate the product, for instance, if the Jacobian is undefined at \(x\) the user need not set p, but should then set eval_status to a non-zero value. 5 The user should compute the product \(p = J_r^T(x) v\), at the point \(x\) indicated in x, between the product of the transpose of the Jacobian \(J_r(x) = \nabla_{x}c_(x)\) with the vector v= \(v\), and then re-enter the function. The result should be set in p, and eval_status should be set to 0. If the user is unable to evaluate the product, for instance, if the Jacobian is undefined at \(x\) the user need not set p, but should then set eval_status to a non-zero value. 6 The user should compute the \(j\)-th column of \(J_r(x)\), with \(j\) provided in index, at the point \(x\) given in x. The resulting nonzeros and their corresponding row indices of the \(j\)-th column of \(J_r(x)\) must be placed in p[1:lp] and ip[1:lp] with lp set accordingly, and eval_status should be set to 0. If the user is unable to evaluate the column, for instance, if the Jacobian is undefined at \(x\) the user need not set p, ip and nz but should then set eval_status to a non-zero value. 7 The user should compute the product \(p = J_r(x) v\) involving the residual Jacobian \(J_r(x)\) at the point \(x\), given in x, and a given sparse vector \(v\), whose nonzeros are in positions iv[lvl:lvu] of v. The resulting \(p\) should be placed in p and eval_status should be set to 0. If the user is unable to evaluate the product, for instance, if the Jacobian is undefined at \(x\) the user need not set p, but should then set eval_status to a non-zero value. 8 The user should compute selected components of the product \(p = J_r^T(x) v\) involving the transpose of the residual Jacobian \(J_r(x)\) at the point \(x\), given in x, and a given vector \(v\). Only components iv[lvl:lvu] of \(p\) should be computed, and recorded in p[iv[lvl:lvu]], and eval_status should be set to 0. If the user is unable to evaluate the product, for instance, if the Jacobian is undefined at \(x\) the user need not set p, but should then set eval_status to a non-zero value. 9 The user has the opportunity to replace the estimate \(x\) in x by an improved value \(x^+\) for which \(f(x^+) \leq f(x)\); in that case r must also be reset to hold \(r(x^+)\).
eval_status	is a scalar variable of type INT that is used to indicate if objective function/gradient/Hessian values can be provided (see above)
n	is a scalar variable of type INT that holds the number of variables
m_r	is a scalar variable of type INT that holds the number of residuals.
m_c	is a scalar variable of type INT that holds the number of cohorts.
x	is a one-dimensional array of size n and type T that holds the values \(x\) of the optimization variables. The j-th component of x, j = 1, … , n, contains \(x_j\).
y	is a one-dimensional array of size n and type T that holds the values \(y\) of the Lagrange multipliers. The i-th component of y, i = 1, … , m_c, contains \(y_i\).
z	is a one-dimensional array of size n and type T that holds the values \(z\) of the dual variables. The j-th component of z, j = 1, … , n, contains \(z_j\).
r	is a one-dimensional array of size m and type T that holds the residual \(r(x)\). The i-th component of r, i = 1, … , m_r, contains \(r_i(x)\). See status = 2, above, for more details.
g	is a one-dimensional array of size n and type T that holds the gradient \(g = \nabla_xf(x)\) of the objective function. The j-th component of g, j = 1, … , n, contains \(g_j\).
x_stat	is a one-dimensional array of size n and type INT that gives the optimal status of the problem variables. If x_stat[j] is negative, variable \(x_j\) most likely lies at its zero, lower bound, while if it is zero, \(x_j\) is free of its bound (or unconstrained).
v	is a one-dimensional array of size max(n,m_r) and type T, that is used for reverse communication. See status = 4, 5, 7 and 8 above for more details.
iv	is a one-dimensional array of size max(n,m_r) and type INT, that is used for reverse communication. See status = 7 and 8 above for more details.
lvl	is a scalar variable of type INT, that is used for reverse communication. See status = 7 and 8 above for more details.
lvu	is a scalar variable of type INT, that is used for reverse communication. See status = 7 and 8 above for more details.
index	is a scalar variable of type INT, that is used for reverse communication. See status = 6 above for more details.
p	is a one-dimensional array of size max(n,m_r) and type T, that is used for reverse communication. See status = 4 to 8 above for more details.
ip	is a one-dimensional array of size n and type INT, that is used for reverse communication. See status = 6 above for more details.
lp	is a scalar variable of type INT, that is used for reverse communication. See status = 6 above for more details.
w	is a one-dimensional array of size m and type T that holds the values \(w\) of the weights on the residuals in the least-squares objective function. It need not be set if the weights are all ones, and in this case can be C_NULL.

    function snls_information(T, INT, data, inform, status)

Provides output information

Parameters:

data	holds private internal data
inform	is a structure containing output information (see snls_inform_type)
status	is a scalar variable of type INT that gives the exit status from the package. Possible values are (currently): 0 The values were recorded successfully

    function snls_terminate(T, INT, data, control, inform)

Deallocate all internal private storage

Parameters:

data	holds private internal data
control	is a structure containing control information (see snls_control_type)
inform	is a structure containing output information (see snls_inform_type)