overview of functions provided

// namespaces

namespace conf;

// typedefs

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

// structs

struct presolve_control_type;
struct presolve_inform_type;

// global functions

void presolve_initialize(
    void **data,
    struct presolve_control_type* control,
    ipc_ *status
);

void presolve_read_specfile(
    struct presolve_control_type* control,
    const char specfile[]
);

void presolve_import_problem(
    struct presolve_control_type* control,
    void **data,
    ipc_ *status,
    ipc_ n,
    ipc_ m,
    const char H_type[],
    ipc_ H_ne,
    const ipc_ H_row[],
    const ipc_ H_col[],
    const ipc_ H_ptr[],
    const rpc_ H_val[],
    const rpc_ g[],
    const rpc_ f,
    const char A_type[],
    ipc_ A_ne,
    const ipc_ A_row[],
    const ipc_ A_col[],
    const ipc_ A_ptr[],
    const rpc_ A_val[],
    const rpc_ c_l[],
    const rpc_ c_u[],
    const rpc_ x_l[],
    const rpc_ x_u[],
    ipc_ *n_out,
    ipc_ *m_out,
    ipc_ *H_ne_out,
    ipc_ *A_ne_out
);

void presolve_transform_problem(
    void **data,
    ipc_ *status,
    ipc_ n,
    ipc_ m,
    ipc_ H_ne,
    ipc_ H_col[],
    ipc_ H_ptr[],
    rpc_ H_val[],
    rpc_ g[],
    rpc_* f,
    ipc_ A_ne,
    ipc_ A_col[],
    ipc_ A_ptr[],
    rpc_ A_val[],
    rpc_ c_l[],
    rpc_ c_u[],
    rpc_ x_l[],
    rpc_ x_u[],
    rpc_ y_l[],
    rpc_ y_u[],
    rpc_ z_l[],
    rpc_ z_u[]
);

void presolve_restore_solution(
    void **data,
    ipc_ *status,
    ipc_ n_in,
    ipc_ m_in,
    const rpc_ x_in[],
    const rpc_ c_in[],
    const rpc_ y_in[],
    const rpc_ z_in[],
    ipc_ n,
    ipc_ m,
    rpc_ x[],
    rpc_ c[],
    rpc_ y[],
    rpc_ z[]
);

void presolve_information(
    void **data,
    struct presolve_inform_type* inform,
    ipc_ *status
);

void presolve_terminate(
    void **data,
    struct presolve_control_type* control,
    struct presolve_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 REAL_32 or (if supported) to __real128 using the variable REAL_128.

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 presolve_initialize(
    void **data,
    struct presolve_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 presolve_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 presolve_read_specfile(
    struct presolve_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/presolve/PRESOLVE.template. See also Table 2.1 in the Fortran documentation provided in $GALAHAD/doc/presolve.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 presolve_control_type)
specfile	is a character string containing the name of the specification file

void presolve_import_problem(
    struct presolve_control_type* control,
    void **data,
    ipc_ *status,
    ipc_ n,
    ipc_ m,
    const char H_type[],
    ipc_ H_ne,
    const ipc_ H_row[],
    const ipc_ H_col[],
    const ipc_ H_ptr[],
    const rpc_ H_val[],
    const rpc_ g[],
    const rpc_ f,
    const char A_type[],
    ipc_ A_ne,
    const ipc_ A_row[],
    const ipc_ A_col[],
    const ipc_ A_ptr[],
    const rpc_ A_val[],
    const rpc_ c_l[],
    const rpc_ c_u[],
    const rpc_ x_l[],
    const rpc_ x_u[],
    ipc_ *n_out,
    ipc_ *m_out,
    ipc_ *H_ne_out,
    ipc_ *A_ne_out
)

Import the initial data, and apply the presolve algorithm to report crucial characteristics of the transformed variant

Parameters:

control	is a struct whose members provide control paramters for the remaining prcedures (see presolve_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 or m > 0 or requirement that a type contains its relevant string ‘dense’, ‘coordinate’, ‘sparse_by_rows’ or ‘diagonal’ has been violated. -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.
m	is a scalar variable of type ipc_, that holds the number of general linear constraints.
H_type	is a one-dimensional array of type char that specifies the symmetric storage scheme used for the Hessian, \(H\). It should be one of ‘coordinate’, ‘sparse_by_rows’, ‘dense’, ‘diagonal’, ‘scaled_identity’, ‘identity’, ‘zero’ or ‘none’, the latter pair if \(H=0\); lower or upper case variants are allowed.
H_ne	is a scalar variable of type ipc_, that holds the number of entries in the lower triangular part of \(H\) in the sparse co-ordinate storage scheme. It need not be set for any of the other schemes.
H_row	is a one-dimensional array of size H_ne and type ipc_, that holds the row indices of the lower triangular part of \(H\) in the sparse co-ordinate storage scheme. It need not be set for any of the other three schemes, and in this case can be NULL.
H_col	is a one-dimensional array of size H_ne and type ipc_, that holds the column indices of the lower triangular part of \(H\) in either the sparse co-ordinate, or the sparse row-wise storage scheme. It need not be set when the dense, diagonal or (scaled) identity storage schemes are used, and in this case can be NULL.
H_ptr	is a one-dimensional array of size n+1 and type ipc_, that holds the starting position of each row of the lower triangular part of \(H\), 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 NULL.
H_val	is a one-dimensional array of size h_ne and type rpc_, that holds the values of the entries of the lower triangular part of the Hessian matrix \(H\) in any of the available storage schemes.
g	is a one-dimensional array of size n and type rpc_, that holds the linear term \(g\) of the objective function. The j-th component of g, j = 0, … , n-1, contains \(g_j\).
f	is a scalar of type rpc_, that holds the constant term \(f\) of the objective function.
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’ or ‘dense; 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 storage scheme. 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 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 NULL.
A_ptr	is a one-dimensional array of size n+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. It need not be set when the other schemes are used, and in this case can be NULL.
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 \(x^l_j\).
n_out	is a scalar variable of type ipc_, that holds the number of variables in the transformed problem.
m_out	is a scalar variable of type ipc_, that holds the number of general linear constraints in the transformed problem.
H_ne_out	is a scalar variable of type ipc_, that holds the number of entries in the lower triangular part of \(H\) in the transformed problem.
A_ne_out	is a scalar variable of type ipc_, that holds the number of entries in \(A\) in the transformed problem.

void presolve_transform_problem(
    void **data,
    ipc_ *status,
    ipc_ n,
    ipc_ m,
    ipc_ H_ne,
    ipc_ H_col[],
    ipc_ H_ptr[],
    rpc_ H_val[],
    rpc_ g[],
    rpc_* f,
    ipc_ A_ne,
    ipc_ A_col[],
    ipc_ A_ptr[],
    rpc_ A_val[],
    rpc_ c_l[],
    rpc_ c_u[],
    rpc_ x_l[],
    rpc_ x_u[],
    rpc_ y_l[],
    rpc_ y_u[],
    rpc_ z_l[],
    rpc_ z_u[]
)

Apply the presolve algorithm to simplify the input problem, and output the transformed variant

Parameters:

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 input values n, m, A_ne or H_ne do not agree with those output as necessary from presolve_import_problem.
n	is a scalar variable of type ipc_, that holds the number of variables in the transformed problem. This must match the value n_out from the last call to presolve_import_problem.
m	is a scalar variable of type ipc_, that holds the number of general linear constraints. This must match the value m_out from the last call to presolve_import_problem.
H_ne	is a scalar variable of type ipc_, that holds the number of entries in the lower triangular part of the transformed \(H\). This must match the value H_ne_out from the last call to presolve_import_problem.
H_col	is a one-dimensional array of size H_ne and type ipc_, that holds the column indices of the lower triangular part of the transformed \(H\) in the sparse row-wise storage scheme.
H_ptr	is a one-dimensional array of size n+1 and type ipc_, that holds the starting position of each row of the lower triangular part of the transformed \(H\) in the sparse row-wise storage scheme.
H_val	is a one-dimensional array of size h_ne and type rpc_, that holds the values of the entries of the lower triangular part of the the transformed Hessian matrix \(H\) in the sparse row-wise storage scheme.
g	is a one-dimensional array of size n and type rpc_, that holds the the transformed linear term \(g\) of the objective function. The j-th component of g, j = 0, … , n-1, contains \(g_j\).
f	is a scalar of type rpc_, that holds the transformed constant term \(f\) of the objective function.
A_ne	is a scalar variable of type ipc_, that holds the number of entries in the transformed \(A\). This must match the value A_ne_out from the last call to presolve_import_problem.
A_col	is a one-dimensional array of size A_ne and type ipc_, that holds the column indices of the transformed \(A\) in the sparse row-wise storage scheme.
A_ptr	is a one-dimensional array of size n+1 and type ipc_, that holds the starting position of each row of the transformed \(A\), as well as the total number of entries, in the sparse row-wise storage scheme.
A_val	is a one-dimensional array of size a_ne and type rpc_, that holds the values of the entries of the transformed constraint Jacobian matrix \(A\) in the sparse row-wise storage scheme.
c_l	is a one-dimensional array of size m and type rpc_, that holds the transformed 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 transformed 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 transformed 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 transformed upper bounds \(x^l\) on the variables \(x\). The j-th component of x_u, j = 0, … , n-1, contains \(x^l_j\).
y_l	is a one-dimensional array of size m and type rpc_, that holds the implied lower bounds \(y^l\) on the transformed Lagrange multipliers \(y\). The i-th component of y_l, i = 0, … , m-1, contains \(y^l_i\).
y_u	is a one-dimensional array of size m and type rpc_, that holds the implied upper bounds \(y^u\) on the transformed Lagrange multipliers \(y\). The i-th component of y_u, i = 0, … , m-1, contains \(y^u_i\).
z_l	is a one-dimensional array of size m and type rpc_, that holds the implied lower bounds \(y^l\) on the transformed dual variables \(z\). The j-th component of z_l, j = 0, … , n-1, contains \(z^l_i\).
z_u	is a one-dimensional array of size m and type rpc_, that holds the implied upper bounds \(y^u\) on the transformed dual variables \(z\). The j-th component of z_u, j = 0, … , n-1, contains \(z^u_i\).

void presolve_restore_solution(
    void **data,
    ipc_ *status,
    ipc_ n_in,
    ipc_ m_in,
    const rpc_ x_in[],
    const rpc_ c_in[],
    const rpc_ y_in[],
    const rpc_ z_in[],
    ipc_ n,
    ipc_ m,
    rpc_ x[],
    rpc_ c[],
    rpc_ y[],
    rpc_ z[]
)

Given the solution (x_in,c_in,y_in,z_in) to the transformed problem, restore to recover the solution (x,c,y,z) to the original

Parameters:

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 input values n, m, n_in and m_in do not agree with those input to and output as necessary from presolve_import_problem.
n_in	is a scalar variable of type ipc_, that holds the number of variables in the transformed problem. This must match the value n_out from the last call to presolve_import_problem.
m_in	is a scalar variable of type ipc_, that holds the number of general linear constraints. This must match the value m_out from the last call to presolve_import_problem.
x_in	is a one-dimensional array of size n_in and type rpc_, that holds the transformed values \(x\) of the optimization variables. The j-th component of x, j = 0, … , n-1, contains \(x_j\).
c_in	is a one-dimensional array of size m and type rpc_, that holds the transformed residual \(c(x)\). The i-th component of c, j = 0, … , n-1, contains \(c_j(x)\).
y_in	is a one-dimensional array of size n_in and type rpc_, that holds the values \(y\) of the transformed Lagrange multipliers for the general linear constraints. The j-th component of y, j = 0, … , n-1, contains \(y_j\).
z_in	is a one-dimensional array of size n_in and type rpc_, that holds the values \(z\) of the transformed dual variables. The j-th component of z, j = 0, … , n-1, contains \(z_j\).
n	is a scalar variable of type ipc_, that holds the number of variables in the transformed problem. This must match the value n as input to presolve_import_problem.
m	is a scalar variable of type ipc_, that holds the number of general linear constraints. This must match the value m as input to presolve_import_problem.
x	is a one-dimensional array of size n and type rpc_, that holds the transformed values \(x\) of the optimization variables. The j-th component of x, j = 0, … , n-1, contains \(x_j\).
c	is a one-dimensional array of size m and type rpc_, that holds the transformed 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 transformed 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 transformed dual variables. The j-th component of z, j = 0, … , n-1, contains \(z_j\).

void presolve_information(
    void **data,
    struct presolve_inform_type* inform,
    ipc_ *status
)

Provides output information

Parameters:

data	holds private internal data
inform	is a struct containing output information (see presolve_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 presolve_terminate(
    void **data,
    struct presolve_control_type* control,
    struct presolve_inform_type* inform
)

Deallocate all internal private storage

Parameters:

data	holds private internal data
control	is a struct containing control information (see presolve_control_type)
inform	is a struct containing output information (see presolve_inform_type)