GALAHAD FDC package#

purpose#

Given an under-determined set of linear equations/constraints \(a_i^T x = b_i^{}\), \(i = 1, \ldots, m\) involving \(n \geq m\) unknowns \(x\), the fdc package determines whether the constraints are consistent, and if so how many of the constraints are dependent; a list of dependent constraints, that is, those which may be removed without changing the solution set, will be found and the remaining \(a_i\) will be linearly independent. Full advantage is taken of any zero coefficients in the matrix \(A\) whose columns are the vectors \(a_i^T\).

See Section 4 of $GALAHAD/doc/fdc.pdf for additional details.

method#

A choice of two methods is available. In the first, the matrix

\[\begin{split}K = \begin{pmatrix}\alpha I & A^T \\ A & 0 \end{pmatrix}\end{split}\]
is formed and factorized for some small \(\alpha > 0\) using the SLS package — the factors \(K = P L D L^T P^T\) are used to determine whether \(A\) has dependent rows. In particular, in exact arithmetic dependencies in \(A\) will correspond to zero pivots in the block diagonal matrix \(D\).

The second choice of method finds factors \(A = P L U Q\) of the rectangular matrix \(A\) using the ULS package. In this case, dependencies in \(A\) will be reflected in zero diagonal entries in \(U\) in exact arithmetic.

The factorization in either case may also be used to determine whether the system is consistent.

matrix storage#

The unsymmetric \(m\) by \(n\) matrix \(A\) must be presented and stored in sparse row-wise storage format. For this, only the nonzero entries are stored, and they are ordered so that those in row i appear directly before those in row i+1. For the i-th row of \(A\) the i-th component of the integer array A_ptr holds the position of the first entry in this row, while A_ptr(m+1) holds the total number of entries plus one. The column indices j, \(1 \leq j \leq n\), and values \(A_{ij}\) of the nonzero entries in the i-th row are stored in components l = A_ptr(i), \(\ldots\), A_ptr(i+1)-1, \(1 \leq i \leq m\), of the integer array A_col, and real array A_val, respectively.

introduction to function calls#

To solve a given problem, functions from the fdc package must be called in the following order:

  • fdc_initialize - provide default control parameters and set up initial data structures

  • fdc_read_specfile (optional) - override control values by reading replacement values from a file

  • fdc_find_dependent_rows - find the number of dependent rows and, if there are any, whether the constraints are independent

  • fdc_terminate - deallocate data structures

See the examples section for illustrations of use.

parametric real type T#

Below, the symbol T refers to a parametric real type that may be Float32 (single precision), Float64 (double precision) or, if supported, Float128 (quadruple precision).

callable functions#

    function fdc_initialize(T, data, control, status)

Set default control values and initialize private data

Parameters:

data

holds private internal data

control

is a structure containing control information (see fdc_control_type)

status

is a scalar variable of type Int32 that gives the exit status from the package. Possible values are (currently):

  • 0

    The import was successful.

    function fdc_read_specfile(T, 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/fdc/FDC.template. See also Table 2.1 in the Fortran documentation provided in $GALAHAD/doc/fdc.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 fdc_control_type)

specfile

is a one-dimensional array of type Vararg{Cchar} that must give the name of the specification file

    function fdc_find_dependent_rows(T, control, data, inform, status,
                                     m, n, A_ne, A_col, A_ptr, A_val, b,
                                     n_depen, depen)

Find dependent rows and, if any, check if \(A x = b\) is consistent

Parameters:

control

is a structure containing control information (see fdc_control_type)

data

holds private internal data

inform

is a structure containing output information (see fdc_inform_type)

status

is a scalar variable of type Int32 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 and m > 0 or requirement that a type contains its relevant string ‘dense’, ‘coordinate’, ‘sparse_by_rows’, ‘diagonal’, ‘scaled_identity’, ‘identity’, ‘zero’ or ‘none’ has been violated.

  • -5

    The constraints appear to be inconsistent.

  • -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.

m

is a scalar variable of type Int32 that holds the number of rows of \(A\).

n

is a scalar variable of type Int32 that holds the number of columns of \(A\).

A_ne

is a scalar variable of type Int32 that holds the number of nonzero entries in \(A\).

A_col

is a one-dimensional array of size A_ne and type Int32 that holds the column indices of \(A\) in a row-wise storage scheme. The nonzeros must be ordered so that those in row i appear directly before those in row i+1, the order within each row is unimportant.

A_ptr

is a one-dimensional array of size n+1 and type Int32 that holds the starting position of each row of \(A\), as well as the total number of entries.

A_val

is a one-dimensional array of size a_ne and type T that holds the values of the entries of the \(A\) ordered as in A_col and A_ptr.

b

is a one-dimensional array of size m and type T that holds the linear term \(b\) in the constraints. The i-th component of b, i = 1, … , m, contains \(b_i\).

n_depen

is a scalar variable of type Int32 that holds the number of dependent constraints, if any.

depen

is a one-dimensional array of size m and type Int32 whose first n_depen components contain the indices of dependent constraints.

    function fdc_terminate(T, data, control, inform)

Deallocate all internal private storage

Parameters:

data

holds private internal data

control

is a structure containing control information (see fdc_control_type)

inform

is a structure containing output information (see fdc_inform_type)

available structures#

fdc_control_type structure#

    struct fdc_control_type{T}
      f_indexing::Bool
      error::Int32
      out::Int32
      print_level::Int32
      indmin::Int32
      valmin::Int32
      pivot_tol::T
      zero_pivot::T
      max_infeas::T
      use_sls::Bool
      scale::Bool
      space_critical::Bool
      deallocate_error_fatal::Bool
      symmetric_linear_solver::NTuple{31,Cchar}
      unsymmetric_linear_solver::NTuple{31,Cchar}
      prefix::NTuple{31,Cchar}
      sls_control::sls_control_type{T}
      uls_control::uls_control_type{T}

detailed documentation#

control derived type as a Julia structure

components#

Bool f_indexing

use C or Fortran sparse matrix indexing

Int32 error

unit for error messages

Int32 out

unit for monitor output

Int32 print_level

controls level of diagnostic output

Int32 indmin

initial estimate of integer workspace for sls (obsolete)

Int32 valmin

initial estimate of real workspace for sls (obsolete)

T pivot_tol

the relative pivot tolerance (obsolete)

T zero_pivot

the absolute pivot tolerance used (obsolete)

T max_infeas

the largest permitted residual

Bool use_sls

choose whether SLS or ULS is used to determine dependencies

Bool scale

should the rows of A be scaled to have unit infinity norm or should no scaling be applied

Bool space_critical

if space is critical, ensure allocated arrays are no bigger than needed

Bool deallocate_error_fatal

exit if any deallocation fails

char symmetric_linear_solver[31]

symmetric (indefinite) linear equation solver

char unsymmetric_linear_solver[31]

unsymmetric linear equation solver

NTuple{31,Cchar} prefix

all output lines will be prefixed by prefix(2:LEN(TRIM(.prefix))-1) where prefix contains the required string enclosed in quotes, e.g. “string” or ‘string’

struct sls_control_type sls_control

control parameters for SLS

struct uls_control_type uls_control

control parameters for ULS

fdc_time_type structure#

    struct fdc_time_type{T}
      total::T
      analyse::T
      factorize::T
      clock_total::T
      clock_analyse::T
      clock_factorize::T

detailed documentation#

time derived type as a Julia structure

components#

T total

the total CPU time spent in the package

T analyse

the CPU time spent analysing the required matrices prior to factorization

T factorize

the CPU time spent factorizing the required matrices

T clock_total

the total clock time spent in the package

T clock_analyse

the clock time spent analysing the required matrices prior to factorization

T clock_factorize

the clock time spent factorizing the required matrices

fdc_inform_type structure#

    struct fdc_inform_type{T}
      status::Int32
      alloc_status::Int32
      bad_alloc::NTuple{81,Cchar}
      factorization_status::Int32
      factorization_integer::Int64
      factorization_real::Int64
      non_negligible_pivot::T
      time::fdc_time_type{T}
      sls_inform::sls_inform_type{T}
      uls_inform::uls_inform_type{T}

detailed documentation#

inform derived type as a Julia structure

components#

Int32 status

return status. See FDC_find_dependent for details

Int32 alloc_status

the status of the last attempted allocation/deallocation

NTuple{81,Cchar} bad_alloc

the name of the array for which an allocation/deallocation error occurred

Int32 factorization_status

the return status from the factorization

Int64 factorization_integer

the total integer workspace required for the factorization

Int64 factorization_real

the total real workspace required for the factorization

T non_negligible_pivot

the smallest pivot which was not judged to be zero when detecting linear dependent constraints

struct fdc_time_type time

timings (see above)

struct sls_inform_type sls_inform

SLS inform type.

struct uls_inform_type uls_inform

ULS inform type.

example calls#

This is an example of how to use the package to find a subset of independent linear constraints; the code is available in $GALAHAD/src/fdc/Julia/test_fdc.jl . A variety of supported Hessian and constraint matrix storage formats are shown.

# test_fdc.jl
# Simple code to test the Julia interface to FDC

using GALAHAD
using Test
using Printf
using Accessors
using Quadmath

function test_fdc(::Type{T}) where T
  # Derived types
  data = Ref{Ptr{Cvoid}}()
  control = Ref{fdc_control_type{T}}()
  inform = Ref{fdc_inform_type{T}}()

  # Set problem data
  m = 3 # number of rows
  n = 4 # number of columns
  A_ne = 10 # number of nonzeros
  A_col = Cint[1, 2, 3, 4, 1, 2, 3, 4, 2, 4]  # column indices
  A_ptr = Cint[1, 5, 9, 11]  # row pointers
  A_val = T[1.0, 2.0, 3.0, 4.0, 2.0, -4.0, 6.0, -8.0, 5.0, 10.0]
  b = T[5.0, 10.0, 0.0]

  # Set output storage
  depen = zeros(Cint, m) # dependencies, if any
  n_depen = Ref{Cint}()
  status = Ref{Cint}()

  @printf(" Fortran sparse matrix indexing\n")

  # Initialize FDC
  fdc_initialize(T, data, control, status)

  # Set user-defined control options
  @reset control[].f_indexing = true # Fortran sparse matrix indexing

  # Start from 0
  fdc_find_dependent_rows(T, control, data, inform, status, m, n, A_ne, A_col, A_ptr, A_val, b,
                          n_depen, depen)

  if status[] == 0
    if n_depen == 0
      @printf("FDC_find_dependent - no dependent rows, status = %i\n", status[])
    else
      @printf("FDC_find_dependent - dependent rows(s):")
      for i in 1:n_depen[]
        @printf(" %i", depen[i])
      end
      @printf(", status = %i\n", status[])
    end
  else
    @printf("FDC_find_dependent - exit status = %1i\n", status[])
  end

  # Delete internal workspace
  fdc_terminate(T, data, control, inform)
  return 0
end

@testset "FDC" begin
  @test test_fdc(Float32) == 0
  @test test_fdc(Float64) == 0
  @test test_fdc(Float128) == 0
end