overview of functions provided#

// typedefs

typedef float spc_;
typedef double rpc_;
typedef int ipc_;

// structs

struct clls_control_type;
struct clls_inform_type;
struct clls_time_type;

// function calls

void clls_initialize(void **data, struct clls_control_type* control, ipc_ *status);
void clls_read_specfile(struct clls_control_type* control, const char specfile[]);

void clls_import(
    struct clls_control_type* control,
    void **data,
    ipc_ *status,
    ipc_ n,
    ipc_ o,
    ipc_ m,
    const char Ao_type[],
    ipc_ Ao_ne,
    const ipc_ Ao_row[],
    const ipc_ Ao_col[],
    ipc_ Ao_ptr_ne,
    const ipc_ Ao_ptr[],
    const char A_type[],
    ipc_ A_ne,
    const ipc_ A_row[],
    const ipc_ A_col[],
    ipc_ A_ptr_ne,
    const ipc_ A_ptr[]
);

void clls_reset_control(
    struct clls_control_type* control,
    void **data,
    ipc_ *status
);

void clls_solve_clls(
    void **data,
    ipc_ *status,
    ipc_ n,
    ipc_ o,
    ipc_ m,
    ipc_ ao_ne,
    const rpc_ A_val[],
    const rpc_ b[],
    rpc_ regularization_weight,
    ipc_ a_ne,
    const rpc_ A_val[],
    const rpc_ c_l[],
    const rpc_ c_u[],
    const rpc_ x_l[],
    const rpc_ x_u[],
    rpc_ x[],
    rpc_ r[],
    rpc_ c[],
    rpc_ y[],
    rpc_ z[],
    ipc_ x_stat[],
    ipc_ c_stat[],
    rpc_ w[]
);

void clls_information(void **data, struct clls_inform_type* inform, ipc_ *status);

void clls_terminate(
    void **data,
    struct clls_control_type* control,
    struct clls_inform_type* inform
);

typedefs#

typedef float spc_

spc_ is real single precision

typedef double rpc_

rpc_ is the real working precision used, but may be changed to float by defining the preprocessor variable SINGLE.

typedef int ipc_

ipc_ is the default integer word length used, but may be changed to int64_t by defining the preprocessor variable INTEGER_64.

function calls#

void clls_initialize(void **data, struct clls_control_type* control, ipc_ *status)

Set default control values and initialize private data

Parameters:

data

holds private internal data

control

is a struct containing control information (see clls_control_type)

status

is a scalar variable of type ipc_, that gives the exit status from the package. Possible values are (currently):

0
The initialization was successful.

void clls_read_specfile(struct clls_control_type* control, const char 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/clls/CLLS.template. See also Table 2.1 in the Fortran documentation provided in $GALAHAD/doc/clls.pdf for a list of how these keywords relate to the components of the control structure.

Parameters:

control	is a struct containing control information (see clls_control_type)
specfile	is a character string containing the name of the specification file

void clls_import(
    struct clls_control_type* control,
    void **data,
    ipc_ *status,
    ipc_ n,
    ipc_ o,
    ipc_ m,
    const char Ao_type[],
    ipc_ Ao_ne,
    const ipc_ Ao_row[],
    const ipc_ Ao_col[],
    ipc_ Ao_ptr_ne,
    const ipc_ Ao_ptr[],
    const char A_type[],
    ipc_ A_ne,
    const ipc_ A_row[],
    const ipc_ A_col[],
    ipc_ A_ptr_ne,
    const ipc_ A_ptr[]
)

Import problem data into internal storage prior to solution.

Parameters:

control	is a struct whose members provide control paramters for the remaining prcedures (see clls_control_type)
data	holds private internal data
status	is a scalar variable of type ipc_, that gives the exit status from the package. Possible values are: 0 The import 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 restrictions n > 0, o > 0 or m > 0 or requirement that a type contains its relevant string ‘coordinate’, ‘sparse_by_rows’, ‘sparse_by_columns’, ‘dense’ or ‘dense_by_columns’ has been violated.
n	is a scalar variable of type ipc_, that holds the number of variables.
o	is a scalar variable of type ipc_, that holds the number of residulas.
m	is a scalar variable of type ipc_, that holds the number of general linear constraints.
Ao_type	is a one-dimensional array of type char that specifies the unsymmetric storage scheme used for the objective design matrix, $A_o$. It should be one of ‘coordinate’, ‘sparse_by_rows’, ‘sparse_by_columns’, ‘dense’ or ‘dense_by_columns’; lower or upper case variants are allowed.
Ao_ne	is a scalar variable of type ipc_, that holds the number of entries in $A_o$ in the sparse co-ordinate storage scheme. It need not be set for any of the other schemes.
Ao_row	is a one-dimensional array of size A_ne and type ipc_, that holds the row indices of $A_o$ in the sparse co-ordinate and sparse column-wise storage schemes. It need not be set for any of the other schemes, and in this case can be NULL.
Ao_col	is a one-dimensional array of size A_ne and type ipc_, that holds the column indices of $A_o$ in the sparse co-ordinate and the sparse row-wise storage schemes. It need not be set for any of the other schemes, and in this case can be NULL.
Ao_ptr_ne	is a scalar variable of type ipc_, that holds the length of the pointer array if sparse row or column storage scheme is used for $A_o$. For the sparse row scheme, Ao_ptr_ne should be at least o+1, while for the sparse column scheme, it should be at least n+1, It need not be set when the other schemes are used.
Ao_ptr	is a one-dimensional array of size o+1 and type ipc_, that holds the starting position of each row of $A_o$, as well as the total number of entries, in the sparse row-wise storage scheme. By contrast, it is a one-dimensional array of size n+1 and type ipc_, that holds the starting position of each column of $A_o$, as well as the total number of entries, in the sparse column-wise storage scheme. It need not be set when the other schemes are used, and in this case can be NULL.
A_type	is a one-dimensional array of type char that specifies the unsymmetric storage scheme used for the constraint Jacobian, $A$. It should be one of ‘coordinate’, ‘sparse_by_rows’, ‘sparse_by_columns’, ‘dense’ or ‘dense_by_columns’; lower or upper case variants are allowed.
A_ne	is a scalar variable of type ipc_, that holds the number of entries in $A$ in the sparse co-ordinate storage scheme. It need not be set for any of the other schemes.
A_row	is a one-dimensional array of size A_ne and type ipc_, that holds the row indices of $A$ in the sparse co-ordinate and sparse column-wise storage schemes. It need not be set for any of the other schemes, and in this case can be NULL.
A_col	is a one-dimensional array of size A_ne and type ipc_, that holds the column indices of $A$ in the sparse co-ordinate and the sparse row-wise storage schemes. It need not be set for any of the other schemes, and in this case can be NULL.
A_ptr_ne	is a scalar variable of type ipc_, that holds the length of the pointer array if sparse row or column storage scheme is used for $A$. For the sparse row scheme, A_ptr_ne should be at least m+1, while for the sparse column scheme, it should be at least n+1, It need not be set when the other schemes are used.
A_ptr	is a one-dimensional array of size m+1 and type ipc_, that holds the starting position of each row of $A$, as well as the total number of entries, in the sparse row-wise storage scheme. By contrast, it is a one-dimensional array of size n+1 and type ipc_, that holds the starting position of each column of $A$, as well as the total number of entries, in the sparse column-wise storage scheme. It need not be set when the other schemes are used, and in this case can be NULL.

void clls_reset_control(
    struct clls_control_type* control,
    void **data,
    ipc_ *status
)

Reset control parameters after import if required.

Parameters:

control

is a struct whose members provide control paramters for the remaining prcedures (see clls_control_type)

data

holds private internal data

status

is a scalar variable of type ipc_, that gives the exit status from the package. Possible values are:

0
The import was successful.

void clls_solve_clls(
    void **data,
    ipc_ *status,
    ipc_ n,
    ipc_ o,
    ipc_ m,
    ipc_ ao_ne,
    const rpc_ Ao_val[],
    const rpc_ b[],
    rpc_ regularization_weight,
    ipc_ a_ne,
    const rpc_ A_val[],
    const rpc_ c_l[],
    const rpc_ c_u[],
    const rpc_ x_l[],
    const rpc_ x_u[],
    rpc_ x[],
    rpc_ r[],
    rpc_ c[],
    rpc_ y[],
    rpc_ z[],
    ipc_ x_stat[],
    ipc_ c_stat[],
    rpc_ w[]
)

Solve the linearly-constrained regularized linear least-squares problem.

Parameters:

data	holds private internal data
status	is a scalar variable of type ipc_, that gives the entry and exit status from the package. 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 restrictions n > 0, o > 0 or m > 0 or requirement that a type contains its relevant string ‘coordinate’, ‘sparse_by_rows’, ‘sparse_by_columns’, ‘dense’ or ‘dense_by_columns’ has been violated. -5 The simple-bound constraints are inconsistent. -7 The constraints appear to have no feasible point. -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. -23 An entry from the strict upper triangle of $H$ has been specified.
n	is a scalar variable of type ipc_, that holds the number of variables.
o	is a scalar variable of type ipc_, that holds the number of residulas.
m	is a scalar variable of type ipc_, that holds the number of general linear constraints.
ao_ne	is a scalar variable of type ipc_, that holds the number of entries in the objective design matrix $A_o$.
Ao_val	is a one-dimensional array of size ao_ne and type rpc_, that holds the values of the entries of the objective design matrix $A_o$ in any of the available storage schemes.
b	is a one-dimensional array of size o and type rpc_, that holds the linear term $b$ of observations. The i-th component of b, i = 0, … , o-1, contains $b_i$.
regularization_weight	is a scalar of type rpc_, that holds the non-negative regularization weight $\sigma \geq 0$.
a_ne	is a scalar variable of type ipc_, that holds the number of entries in the constraint Jacobian matrix $A$.
A_val	is a one-dimensional array of size a_ne and type rpc_, that holds the values of the entries of the constraint Jacobian matrix $A$ in any of the available storage schemes.
c_l	is a one-dimensional array of size m and type rpc_, that holds the lower bounds $c^l$ on the constraints $A x$. The i-th component of c_l, i = 0, … , m-1, contains $c^l_i$.
c_u	is a one-dimensional array of size m and type rpc_, that holds the upper bounds $c^l$ on the constraints $A x$. The i-th component of c_u, i = 0, … , m-1, contains $c^u_i$.
x_l	is a one-dimensional array of size n and type rpc_, that holds the lower bounds $x^l$ on the variables $x$. The j-th component of x_l, j = 0, … , n-1, contains $x^l_j$.
x_u	is a one-dimensional array of size n and type rpc_, that holds the upper bounds $x^l$ on the variables $x$. The j-th component of x_u, j = 0, … , n-1, contains $x^l_j$.
x	is a one-dimensional array of size n and type rpc_, that holds the values $x$ of the optimization variables. The j-th component of x, j = 0, … , n-1, contains $x_j$.
r	is a one-dimensional array of size o and type rpc_, that holds the residuals $r(x)=A_0x-b$. The i-th component of r, i = 0, … , o-1, contains $r_i$.
c	is a one-dimensional array of size m and type rpc_, that holds the residual $c(x)$. The i-th component of c, j = 0, … , n-1, contains $c_j(x)$.
y	is a one-dimensional array of size n and type rpc_, that holds the values $y$ of the Lagrange multipliers for the general linear constraints. The j-th component of y, j = 0, … , n-1, contains $y_j$.
z	is a one-dimensional array of size n and type rpc_, that holds the values $z$ of the dual variables. The j-th component of z, j = 0, … , n-1, contains $z_j$.
x_stat	is a one-dimensional array of size n and type ipc_, that gives the optimal status of the problem variables. If x_stat(j) is negative, the variable $x_j$ most likely lies on its lower bound, if it is positive, it lies on its upper bound, and if it is zero, it lies between its bounds.
c_stat	is a one-dimensional array of size m and type ipc_, that gives the optimal status of the general linear constraints. If c_stat(i) is negative, the constraint value $a_i^Tx$ most likely lies on its lower bound, if it is positive, it lies on its upper bound, and if it is zero, it lies between its bounds.
w	is a one-dimensional array of size o and type rpc_, that holds the values $w$ of strictly-positive observation weights. The i-th component of w, i = 0, … , o-1, contains $w_i$. If the weights are all one, w can be set to NULL.

void clls_information(void **data, struct clls_inform_type* inform, ipc_ *status)

Provides output information

Parameters:

data

holds private internal data

inform

is a struct containing output information (see clls_inform_type)

status

is a scalar variable of type ipc_, that gives the exit status from the package. Possible values are (currently):

0
The values were recorded successfully

void clls_terminate(
    void **data,
    struct clls_control_type* control,
    struct clls_inform_type* inform
)

Deallocate all internal private storage

Parameters:

data	holds private internal data
control	is a struct containing control information (see clls_control_type)
inform	is a struct containing output information (see clls_inform_type)

data	holds private internal data
status	is a scalar variable of type ipc_, that gives the entry and exit status from the package. 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 restrictions n > 0, o > 0 or m > 0 or requirement that a type contains its relevant string ‘coordinate’, ‘sparse_by_rows’, ‘sparse_by_columns’, ‘dense’ or ‘dense_by_columns’ has been violated. -5 The simple-bound constraints are inconsistent. -7 The constraints appear to have no feasible point. -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. -23 An entry from the strict upper triangle of \(H\) has been specified.
n	is a scalar variable of type ipc_, that holds the number of variables.
o	is a scalar variable of type ipc_, that holds the number of residulas.
m	is a scalar variable of type ipc_, that holds the number of general linear constraints.
ao_ne	is a scalar variable of type ipc_, that holds the number of entries in the objective design matrix \(A_o\).
Ao_val	is a one-dimensional array of size ao_ne and type rpc_, that holds the values of the entries of the objective design matrix \(A_o\) in any of the available storage schemes.
b	is a one-dimensional array of size o and type rpc_, that holds the linear term \(b\) of observations. The i-th component of b, i = 0, … , o-1, contains \(b_i\).
regularization_weight	is a scalar of type rpc_, that holds the non-negative regularization weight \(\sigma \geq 0\).
a_ne	is a scalar variable of type ipc_, that holds the number of entries in the constraint Jacobian matrix \(A\).
A_val	is a one-dimensional array of size a_ne and type rpc_, that holds the values of the entries of the constraint Jacobian matrix \(A\) in any of the available storage schemes.
c_l	is a one-dimensional array of size m and type rpc_, that holds the lower bounds \(c^l\) on the constraints \(A x\). The i-th component of c_l, i = 0, … , m-1, contains \(c^l_i\).
c_u	is a one-dimensional array of size m and type rpc_, that holds the upper bounds \(c^l\) on the constraints \(A x\). The i-th component of c_u, i = 0, … , m-1, contains \(c^u_i\).
x_l	is a one-dimensional array of size n and type rpc_, that holds the lower bounds \(x^l\) on the variables \(x\). The j-th component of x_l, j = 0, … , n-1, contains \(x^l_j\).
x_u	is a one-dimensional array of size n and type rpc_, that holds the upper bounds \(x^l\) on the variables \(x\). The j-th component of x_u, j = 0, … , n-1, contains \(x^l_j\).
x	is a one-dimensional array of size n and type rpc_, that holds the values \(x\) of the optimization variables. The j-th component of x, j = 0, … , n-1, contains \(x_j\).
r	is a one-dimensional array of size o and type rpc_, that holds the residuals \(r(x)=A_0x-b\). The i-th component of r, i = 0, … , o-1, contains \(r_i\).
c	is a one-dimensional array of size m and type rpc_, that holds the residual \(c(x)\). The i-th component of c, j = 0, … , n-1, contains \(c_j(x)\).
y	is a one-dimensional array of size n and type rpc_, that holds the values \(y\) of the Lagrange multipliers for the general linear constraints. The j-th component of y, j = 0, … , n-1, contains \(y_j\).
z	is a one-dimensional array of size n and type rpc_, that holds the values \(z\) of the dual variables. The j-th component of z, j = 0, … , n-1, contains \(z_j\).
x_stat	is a one-dimensional array of size n and type ipc_, that gives the optimal status of the problem variables. If x_stat(j) is negative, the variable \(x_j\) most likely lies on its lower bound, if it is positive, it lies on its upper bound, and if it is zero, it lies between its bounds.
c_stat	is a one-dimensional array of size m and type ipc_, that gives the optimal status of the general linear constraints. If c_stat(i) is negative, the constraint value \(a_i^Tx\) most likely lies on its lower bound, if it is positive, it lies on its upper bound, and if it is zero, it lies between its bounds.
w	is a one-dimensional array of size o and type rpc_, that holds the values \(w\) of strictly-positive observation weights. The i-th component of w, i = 0, … , o-1, contains \(w_i\). If the weights are all one, w can be set to NULL.