GALAHAD ARC package#
purpose#
The arc package uses a regularization method to find a (local)
minimizer of a differentiable objective function \(f(x)\) of
many variables \(x\). The method offers the choice of direct
and iterative solution of the key subproblems, and is most
suitable for large problems. First derivatives are required, and
if second derivatives can be calculated, they will be exploited.
See Section 4 of $GALAHAD/doc/arc.pdf for additional details.
method#
An adaptive cubic regularization method is used. In this, an improvement to a current estimate of the required minimizer, \(x_k\) is sought by computing a step \(s_k\). The step is chosen to approximately minimize a model \(m_k(s)\) of \(f(x_k + s)\) that includes a weighted term \(\sigma_k \|s_k\|^3\) for some specified positive weight \(\sigma_k\). The quality of the resulting step \(s_k\) is assessed by computing the “ratio” \((f(x_k) - f(x_k + s_k))/ (m_k(0) - m_k(s_k))\). The step is deemed to have succeeded if the ratio exceeds a given \(\eta_s > 0\), and in this case \(x_{k+1} = x_k + s_k\). Otherwise \(x_{k+1} = x_k\), and the weight is increased by powers of a given increase factor up to a given limit. If the ratio is larger than \(\eta_v \geq \eta_d\), the weight will be decreased by powers of a given decrease factor again up to a given limit. The method will terminate as soon as \(\|\nabla_x f(x_k)\|\) is smaller than a specified value.
Either linear or quadratic models \(m_k(s)\) may be used. The former will be taken as the first two terms \(f(x_k) + s^T \nabla_x f(x_k)\) of a Taylor series about \(x_k\), while the latter uses an approximation to the first three terms \(f(x_k) + s^T \nabla_x f(x_k) + \frac{1}{2} s^T B_k s\), for which \(B_k\) is a symmetric approximation to the Hessian \(\nabla_{xx} f(x_k)\); possible approximations include the true Hessian, limited-memory secant and sparsity approximations and a scaled identity matrix. Normally a two-norm regularization will be used, but this may change if preconditioning is employed.
An approximate minimizer of the cubic model
is found using either a direct approach involving factorization or an
iterative (conjugate-gradient/Lanczos) approach based on approximations
to the required solution from a so-called Krlov subspace. The direct
approach is based on the knowledge that the required solution
satisfies the linear system of equations \((B_k + \lambda_k I) s_k
= - \nabla_x f(x_k)\) involving a scalar Lagrange multiplier \(\lambda_k\).
This multiplier is found by uni-variate root finding, using a safeguarded
Newton-like process, by RQS or DPS
(depending on the norm chosen). The iterative approach
uses GLRT, and is best accelerated by preconditioning
with good approximations to \(B_k\) using PSLS. The
iterative approach has the advantage that only matrix-vector products
\(B_k v\) are required, and thus \(B_k\) is not required explicitly.
However when factorizations of \(B_k\) are possible, the direct approach
is often more efficient.
references#
The generic adaptive cubic regularization method is described in detail in
C. Cartis, N. I. M. Gould and Ph. L. Toint, ``Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results’’ Mathematical Programming 127(2) (2011) 245–295,
and uses ``tricks’’ as suggested in
N. I. M. Gould, M. Porcelli and Ph. L. Toint, ``Updating the regularization parameter in the adaptive cubic regularization algorithm’’. Computational Optimization and Applications 53(1) (2012) 1-22.
matrix storage#
The symmetric \(n\) by \(n\) matrix \(H = \nabla^2_{xx}f\) may be presented and stored in a variety of formats. But crucially symmetry is exploited by only storing values from the lower triangular part (i.e, those entries that lie on or below the leading diagonal).
Dense storage format: The matrix \(H\) is stored as a compact dense matrix by rows, that is, the values of the entries of each row in turn are stored in order within an appropriate real one-dimensional array. Since \(H\) is symmetric, only the lower triangular part (that is the part \(H_{ij}\) for \(1 \leq j \leq i \leq n\)) need be held. In this case the lower triangle should be stored by rows, that is component \((i-1) * i / 2 + j\) of the storage array H_val will hold the value \(H_{ij}\) (and, by symmetry, \(H_{ji}\)) for \(1 \leq j \leq i \leq n\). The string H_type = ‘dense’ should be specified.
Sparse co-ordinate storage format: Only the nonzero entries of the matrices are stored. For the \(l\)-th entry, \(1 \leq l \leq ne\), of \(H\), its row index i, column index j and value \(H_{ij}\), \(1 \leq j \leq i \leq n\), are stored as the \(l\)-th components of the integer arrays H_row and H_col and real array H_val, respectively, while the number of nonzeros is recorded as H_ne = \(ne\). Note that only the entries in the lower triangle should be stored. The string H_type = ‘coordinate’ should be specified.
Sparse row-wise storage format: Again only the nonzero entries are stored, but this time they are ordered so that those in row i appear directly before those in row i+1. For the i-th row of \(H\) the i-th component of the integer array H_ptr holds the position of the first entry in this row, while H_ptr(n+1) holds the total number of entries plus one. The column indices j, \(1 \leq j \leq i\), and values \(H_{ij}\) of the entries in the i-th row are stored in components l = H_ptr(i), …, H_ptr(i+1)-1 of the integer array H_col, and real array H_val, respectively. Note that as before only the entries in the lower triangle should be stored. For sparse matrices, this scheme almost always requires less storage than its predecessor. The string H_type = ‘sparse_by_rows’ should be specified.
Diagonal storage format: If \(H\) is diagonal (i.e., \(H_{ij} = 0\) for all \(1 \leq i \neq j \leq n\)) only the diagonals entries \(H_{ii}\), \(1 \leq i \leq n\) need be stored, and the first n components of the array H_val may be used for the purpose. The string H_type = ‘diagonal’ should be specified.
introduction to function calls#
To solve a given problem, functions from the arc package must be called in the following order:
arc_initialize - provide default control parameters and set up initial data structures
arc_read_specfile (optional) - override control values by reading replacement values from a file
arc_import - set up problem data structures and fixed values
arc_reset_control (optional) - possibly change control parameters if a sequence of problems are being solved
solve the problem by calling one of
arc_solve_with_mat - solve using function calls to evaluate function, gradient and Hessian values
arc_solve_without_mat - solve using function calls to evaluate function and gradient values and Hessian-vector products
arc_solve_reverse_with_mat - solve returning to the calling program to obtain function, gradient and Hessian values, or
arc_solve_reverse_without_mat - solve returning to the calling prorgram to obtain function and gradient values and Hessian-vector products
arc_information (optional) - recover information about the solution and solution process
arc_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) or Float64 (double precision).
callable functions#
function arc_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 arc_control_type) |
status |
is a scalar variable of type Int32 that gives the exit status from the package. Possible values are (currently):
|
function arc_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/arc/ARC.template. See also Table 2.1 in the Fortran documentation provided in $GALAHAD/doc/arc.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 arc_control_type) |
specfile |
is a one-dimensional array of type Vararg{Cchar} that must give the name of the specification file |
function arc_import(T, control, data, status, n, H_type, ne, H_row, H_col, H_ptr)
Import problem data into internal storage prior to solution.
Parameters:
control |
is a structure whose members provide control parameters for the remaining procedures (see arc_control_type) |
data |
holds private internal data |
status |
is a scalar variable of type Int32 that gives the exit status from the package. Possible values are:
|
n |
is a scalar variable of type Int32 that holds the number of variables |
H_type |
is a one-dimensional array of type Vararg{Cchar} that specifies the symmetric storage scheme used for the Hessian. It should be one of ‘coordinate’, ‘sparse_by_rows’, ‘dense’, ‘diagonal’ or ‘absent’, the latter if access to the Hessian is via matrix-vector products; lower or upper case variants are allowed |
ne |
is a scalar variable of type Int32 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 three schemes. |
H_row |
is a one-dimensional array of size ne and type Int32 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 C_NULL |
H_col |
is a one-dimensional array of size ne and type Int32 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 or diagonal storage schemes are used, and in this case can be C_NULL |
H_ptr |
is a one-dimensional array of size n+1 and type Int32 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 C_NULL |
function arc_reset_control(T, 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 arc_control_type) |
data |
holds private internal data |
status |
is a scalar variable of type Int32 that gives the exit status from the package. Possible values are:
|
function arc_solve_with_mat(T, data, userdata, status, n, x, g, ne, eval_f, eval_g, eval_h, eval_prec)
Find a local minimizer of a given function using a regularization method.
This call is for the case where \(H = \nabla_{xx}f(x)\) is provided specifically, and all function/derivative information 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 Int32 that gives the entry and exit status from the package. On initial entry, status must be set to 1. Possible exit values are:
|
n |
is a scalar variable of type Int32 that holds the number of variables |
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 |
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 |
ne |
is a scalar variable of type Int32 that holds the number of entries in the lower triangular part of the Hessian matrix \(H\). |
eval_f |
is a user-supplied function that must have the following signature: function eval_f(n, x, f, userdata) The value of the objective function \(f(x)\) evaluated
at x=\(x\) must be assigned to f, 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_g |
is a user-supplied function that must have the following signature: function eval_g(n, x, g, userdata) The components of the gradient \(g = \nabla_x f(x\)) of
the objective function evaluated at x=\(x\) must be
assigned to g, 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_h |
is a user-supplied function that must have the following signature: function eval_h(n, ne, x, h, userdata) The nonzeros of the Hessian \(H = \nabla_{xx}f(x)\) of
the objective function evaluated at x=\(x\) must be
assigned to h in the same order as presented to
arc_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_prec |
is an optional user-supplied function that may be C_NULL. If non-NULL, it must have the following signature: function eval_prec(n, x, u, v, userdata) The product \(u = P(x) v\) of the user’s preconditioner
\(P(x)\) evaluated at \(x\) with the vector v=\(v\), the
result \(u\) must be retured in u, 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 |
function arc_solve_without_mat(T, data, userdata, status, n, x, g, eval_f, eval_g, eval_hprod, eval_prec)
Find a local minimizer of a given function using a regularization method.
This call is for the case where access to \(H = \nabla_{xx}f(x)\) is provided by Hessian-vector products, and all function/derivative information 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 Int32 that gives the entry and exit status from the package. On initial entry, status must be set to 1. Possible exit values are:
|
n |
is a scalar variable of type Int32 that holds the number of variables |
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 |
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 |
eval_f |
is a user-supplied function that must have the following signature: function eval_f(n, x, f, userdata) The value of the objective function \(f(x)\) evaluated
at x=\(x\) must be assigned to f, 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_g |
is a user-supplied function that must have the following signature: function eval_g(n, x, g, userdata) The components of the gradient \(g = \nabla_x f(x\)) of
the objective function evaluated at x=\(x\) must be
assigned to g, 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_hprod |
is a user-supplied function that must have the following signature: function eval_hprod(n, x, u, v, got_h, userdata) The sum \(u + \nabla_{xx}f(x) v\) of the product of the
Hessian \(\nabla_{xx}f(x)\) of the objective function
evaluated at x=\(x\) with the vector v=\(v\) and the
vector $ \(u\) must be returned in u, and the function
return value set to 0. If the evaluation is impossible
at x, return should be set to a nonzero value. The
Hessian has already been evaluated or used at x if
got_h is true. Data may be passed into |
eval_prec |
is an optional user-supplied function that may be C_NULL. If non-NULL, it must have the following signature: function eval_prec(n, x, u, v, userdata) The product \(u = P(x) v\) of the user’s preconditioner
\(P(x)\) evaluated at \(x\) with the vector v=\(v\), the
result \(u\) must be retured in u, 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 |
function arc_solve_reverse_with_mat(T, data, status, eval_status, n, x, f, g, ne, H_val, u, v)
Find a local minimizer of a given function using a regularization method.
This call is for the case where \(H = \nabla_{xx}f(x)\) is provided specifically, but function/derivative information is only available by returning to the calling procedure
Parameters:
data |
holds private internal data |
status |
is a scalar variable of type Int32 that gives the entry and exit status from the package. On initial entry, status must be set to 1. Possible exit values are:
|
eval_status |
is a scalar variable of type Int32 that is used to indicate if objective function/gradient/Hessian values can be provided (see above) |
n |
is a scalar variable of type Int32 that holds the number of variables |
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 |
f |
is a scalar variable pointer of type T that holds the value of the objective function. |
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 |
ne |
is a scalar variable of type Int32 that holds the number of entries in the lower triangular part of the Hessian matrix \(H\). |
H_val |
is a one-dimensional array of size ne and type T that holds the values of the entries of the lower triangular part of the Hessian matrix \(H\) in any of the available storage schemes. |
u |
is a one-dimensional array of size n and type T that is used for reverse communication (see above for details) |
v |
is a one-dimensional array of size n and type T that is used for reverse communication (see above for details) |
function arc_solve_reverse_without_mat(T, data, status, eval_status, n, x, f, g, u, v)
Find a local minimizer of a given function using a regularization method.
This call is for the case where access to \(H = \nabla_{xx}f(x)\) is provided by Hessian-vector products, but function/derivative information is only available by returning to the calling procedure.
Parameters:
data |
holds private internal data |
status |
is a scalar variable of type Int32 that gives the entry and exit status from the package. On initial entry, status must be set to 1. Possible exit values are:
|
eval_status |
is a scalar variable of type Int32 that is used to indicate if objective function/gradient/Hessian values can be provided (see above) |
n |
is a scalar variable of type Int32 that holds the number of variables |
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 |
f |
is a scalar variable pointer of type T that holds the value of the objective function. |
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 |
u |
is a one-dimensional array of size n and type T that is used for reverse communication (see above for details) |
v |
is a one-dimensional array of size n and type T that is used for reverse communication (see above for details) |
function arc_information(T, data, inform, status)
Provides output information
Parameters:
data |
holds private internal data |
inform |
is a structure containing output information (see arc_inform_type) |
status |
is a scalar variable of type Int32 that gives the exit status from the package. Possible values are (currently):
|
function arc_terminate(T, data, control, inform)
Deallocate all internal private storage
Parameters:
data |
holds private internal data |
control |
is a structure containing control information (see arc_control_type) |
inform |
is a structure containing output information (see arc_inform_type) |
available structures#
arc_control_type structure#
struct arc_control_type{T} f_indexing::Bool error::Int32 out::Int32 print_level::Int32 start_print::Int32 stop_print::Int32 print_gap::Int32 maxit::Int32 alive_unit::Int32 alive_file::NTuple{31,Cchar} non_monotone::Int32 model::Int32 norm::Int32 semi_bandwidth::Int32 lbfgs_vectors::Int32 max_dxg::Int32 icfs_vectors::Int32 mi28_lsize::Int32 mi28_rsize::Int32 advanced_start::Int32 stop_g_absolute::T stop_g_relative::T stop_s::T initial_weight::T minimum_weight::T reduce_gap::T tiny_gap::T large_root::T eta_successful::T eta_very_successful::T eta_too_successful::T weight_decrease_min::T weight_decrease::T weight_increase::T weight_increase_max::T obj_unbounded::T cpu_time_limit::T clock_time_limit::T hessian_available::Bool subproblem_direct::Bool renormalize_weight::Bool quadratic_ratio_test::Bool space_critical::Bool deallocate_error_fatal::Bool prefix::NTuple{31,Cchar} rqs_control::rqs_control_type{T} glrt_control::glrt_control_type{T} dps_control::dps_control_type{T} psls_control::psls_control_type{T} lms_control::lms_control_type{T} lms_control_prec::lms_control_type{T} sha_control::sha_control_type
detailed documentation#
control derived type as a Julia structure
components#
Bool f_indexing
use C or Fortran sparse matrix indexing
Int32 error
error and warning diagnostics occur on stream error
Int32 out
general output occurs on stream out
Int32 print_level
the level of output required.
\(\leq\) 0 gives no output,
= 1 gives a one-line summary for every iteration,
= 2 gives a summary of the inner iteration for each iteration,
\(\geq\) 3 gives increasingly verbose (debugging) output
Int32 start_print
any printing will start on this iteration
Int32 stop_print
any printing will stop on this iteration
Int32 print_gap
the number of iterations between printing
Int32 maxit
the maximum number of iterations performed
Int32 alive_unit
removal of the file alive_file from unit alive_unit terminates execution
char alive_file[31]
see alive_unit
Int32 non_monotone
the descent strategy used.
Possible values are
<= 0 a monotone strategy is used.
anything else, a non-monotone strategy with history length .non_monotine is used.
Int32 model
the model used.
Possible values are
0 dynamic (not yet implemented)
1 first-order (no Hessian)
2 second-order (exact Hessian)
3 barely second-order (identity Hessian)
4 secant second-order (limited-memory BFGS, with .lbfgs_vectors history) (not yet implemented)
5 secant second-order (limited-memory SR1, with .lbfgs_vectors history) (not yet implemented)
Int32 norm
the regularization norm used.
The norm is defined via \(\|v\|^2 = v^T P v\), and will define the preconditioner used for iterative methods. Possible values for \(P\) are
-3 users own preconditioner
-2 \(P =\) limited-memory BFGS matrix (with .lbfgs_vectors history)
-1 identity (= Euclidan two-norm)
0 automatic (not yet implemented)
1 diagonal, \(P =\) diag( max( Hessian, .min_diagonal ) )
2 banded, \(P =\) band( Hessian ) with semi-bandwidth .semi_bandwidth
3 re-ordered band, P=band(order(A)) with semi-bandwidth .semi_bandwidth
4 full factorization, \(P =\) Hessian, Schnabel-Eskow modification
5 full factorization, \(P =\) Hessian, GMPS modification (not yet implemented)
6 incomplete factorization of Hessian, Lin-More’
7 incomplete factorization of Hessian, HSL_MI28
8 incomplete factorization of Hessian, Munskgaard (not yet implemented)
9 expanding band of Hessian (not yet implemented)
10 diagonalizing norm from GALAHAD_DPS (subproblem_direct only)
Int32 semi_bandwidth
specify the semi-bandwidth of the band matrix P if required
Int32 lbfgs_vectors
number of vectors used by the L-BFGS matrix P if required
Int32 max_dxg
number of vectors used by the sparsity-based secant Hessian if required
Int32 icfs_vectors
number of vectors used by the Lin-More’ incomplete factorization matrix P if required
Int32 mi28_lsize
the maximum number of fill entries within each column of the incomplete factor L computed by HSL_MI28. In general, increasing .mi28_lsize improve the quality of the preconditioner but increases the time to compute and then apply the preconditioner. Values less than 0 are treated as 0
Int32 mi28_rsize
the maximum number of entries within each column of the strictly lower triangular matrix \(R\) used in the computation of the preconditioner by HSL_MI28. Rank-1 arrays of size .mi28_rsize \* n are allocated internally to hold \(R\). Thus the amount of memory used, as well as the amount of work involved in computing the preconditioner, depends on .mi28_rsize. Setting .mi28_rsize > 0 generally leads to a higher quality preconditioner than using .mi28_rsize = 0, and choosing .mi28_rsize >= .mi28_lsize is generally recommended
Int32 advanced_start
try to pick a good initial regularization weight using .advanced_start iterates of a variant on the strategy of Sartenaer SISC 18(6) 1990:1788-1803
T stop_g_absolute
overall convergence tolerances. The iteration will terminate when the norm of the gradient of the objective function is smaller than MAX( .stop_g_absolute, .stop_g_relative * norm of the initial gradient ) or if the step is less than .stop_s
T stop_g_relative
see stop_g_absolute
T stop_s
see stop_g_absolute
T initial_weight
Initial value for the regularisation weight (-ve => 1/||g_0||)
T minimum_weight
minimum permitted regularisation weight
T reduce_gap
expert parameters as suggested in Gould, Porcelli & Toint, “Updating the regularization parameter in the adaptive cubic regularization algorithm” RAL-TR-2011-007, Rutherford Appleton Laboratory, England (2011), http://epubs.stfc.ac.uk/bitstream/6181/RAL-TR-2011-007.pdf (these are denoted beta, epsilon_chi and alpha_max in the paper)
T tiny_gap
see reduce_gap
T large_root
see reduce_gap
T eta_successful
a potential iterate will only be accepted if the actual decrease f - f(x_new) is larger than .eta_successful times that predicted by a quadratic model of the decrease. The regularization weight will be decreased if this relative decrease is greater than .eta_very_successful but smaller than .eta_too_successful (the first is eta in Gould, Porcell and Toint, 2011)
T eta_very_successful
see eta_successful
T eta_too_successful
see eta_successful
T weight_decrease_min
on very successful iterations, the regularization weight will be reduced by the factor .weight_decrease but no more than .weight_decrease_min while if the iteration is unsuccessful, the weight will be increased by a factor .weight_increase but no more than .weight_increase_max (these are delta_1, delta_2, delta3 and delta_max in Gould, Porcelli and Toint, 2011)
T weight_decrease
see weight_decrease_min
T weight_increase
see weight_decrease_min
T weight_increase_max
see weight_decrease_min
T obj_unbounded
the smallest value the onjective function may take before the problem is marked as unbounded
T cpu_time_limit
the maximum CPU time allowed (-ve means infinite)
T clock_time_limit
the maximum elapsed clock time allowed (-ve means infinite)
Bool hessian_available
is the Hessian matrix of second derivatives available or is access only via matrix-vector products?
Bool subproblem_direct
use a direct (factorization) or (preconditioned) iterative method to find the search direction
Bool renormalize_weight
should the weight be renormalized to account for a change in preconditioner?
Bool quadratic_ratio_test
should the test for acceptance involve the quadratic model or the cubic?
Bool space_critical
if .space_critical true, every effort will be made to use as little space as possible. This may result in longer computation time
Bool deallocate_error_fatal
if .deallocate_error_fatal is true, any array/pointer deallocation error will terminate execution. Otherwise, computation will continue
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 rqs_control_type rqs_control
control parameters for RQS
struct glrt_control_type glrt_control
control parameters for GLRT
struct dps_control_type dps_control
control parameters for DPS
struct psls_control_type psls_control
control parameters for PSLS
struct lms_control_type lms_control
control parameters for LMS
struct lms_control_type lms_control_prec
control parameters for LMS used for preconditioning
struct sha_control_type sha_control
control parameters for SHA
arc_time_type structure#
struct arc_time_type{T} total::Float32 preprocess::Float32 analyse::Float32 factorize::Float32 solve::Float32 clock_total::T clock_preprocess::T clock_analyse::T clock_factorize::T clock_solve::T
detailed documentation#
time derived type as a Julia structure
components#
Float32 total
the total CPU time spent in the package
Float32 preprocess
the CPU time spent preprocessing the problem
Float32 analyse
the CPU time spent analysing the required matrices prior to factorizatio
Float32 factorize
the CPU time spent factorizing the required matrices
Float32 solve
the CPU time spent computing the search direction
T clock_total
the total clock time spent in the package
T clock_preprocess
the clock time spent preprocessing the problem
T clock_analyse
the clock time spent analysing the required matrices prior to factorizat
T clock_factorize
the clock time spent factorizing the required matrices
T clock_solve
the clock time spent computing the search direction
arc_inform_type structure#
struct arc_inform_type{T} status::Int32 alloc_status::Int32 bad_alloc::NTuple{81,Cchar} iter::Int32 cg_iter::Int32 f_eval::Int32 g_eval::Int32 h_eval::Int32 factorization_status::Int32 factorization_max::Int32 max_entries_factors::Int64 factorization_integer::Int64 factorization_real::Int64 factorization_average::T obj::T norm_g::T weight::T time::arc_time_type{T} rqs_inform::rqs_inform_type{T} glrt_inform::glrt_inform_type{T} dps_inform::dps_inform_type{T} psls_inform::psls_inform_type{T} lms_inform::lms_inform_type{T} lms_inform_prec::lms_inform_type{T} sha_inform::sha_inform_type
detailed documentation#
inform derived type as a Julia structure
components#
Int32 status
return status. See ARC_solve 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 iter
the total number of iterations performed
Int32 cg_iter
the total number of CG iterations performed
Int32 f_eval
the total number of evaluations of the objective function
Int32 g_eval
the total number of evaluations of the gradient of the objective functio
Int32 h_eval
the total number of evaluations of the Hessian of the objective function
Int32 factorization_status
the return status from the factorization
Int32 factorization_max
the maximum number of factorizations in a sub-problem solve
Int64 max_entries_factors
the maximum number of entries in the factors
Int64 factorization_integer
the total integer workspace required for the factorization
Int64 factorization_real
the total real workspace required for the factorization
T factorization_average
the average number of factorizations per sub-problem solve
T obj
the value of the objective function at the best estimate of the solution determined by the package.
T norm_g
the norm of the gradient of the objective function at the best estimate of the solution determined by the package.
T weight
the current value of the regularization weight
struct arc_time_type time
timings (see above)
struct rqs_inform_type rqs_inform
inform parameters for RQS
struct glrt_inform_type glrt_inform
inform parameters for GLRT
struct dps_inform_type dps_inform
inform parameters for DPS
struct psls_inform_type psls_inform
inform parameters for PSLS
struct lms_inform_type lms_inform
inform parameters for LMS
struct lms_inform_type lms_inform_prec
inform parameters for LMS used for preconditioning
struct sha_inform_type sha_inform
inform parameters for SHA
example calls#
This is an example of how to use the package to minimize a multi-dimensional objective function; the code is available in $GALAHAD/src/arc/Julia/test_arc.jl . A variety of supported Hessian and constraint matrix storage formats are shown.
# test_arc.jl
# Simple code to test the Julia interface to ARC
using GALAHAD
using Test
using Printf
using Accessors
# Custom userdata struct
struct userdata_arc{T}
p::T
end
function test_arc(::Type{T}) where T
# Objective function
function fun(n::Int, x::Vector{T}, f::Ref{T}, userdata::userdata_arc)
p = userdata.p
f[] = (x[1] + x[3] + p)^2 + (x[2] + x[3])^2 + cos(x[1])
return 0
end
# Gradient of the objective
function grad(n::Int, x::Vector{T}, g::Vector{T}, userdata::userdata_arc)
p = userdata.p
g[1] = 2.0 * (x[1] + x[3] + p) - sin(x[1])
g[2] = 2.0 * (x[2] + x[3])
g[3] = 2.0 * (x[1] + x[3] + p) + 2.0 * (x[2] + x[3])
return 0
end
# Hessian of the objective
function hess(n::Int, ne::Int, x::Vector{T}, hval::Vector{T},
userdata::userdata_arc)
hval[1] = 2.0 - cos(x[1])
hval[2] = 2.0
hval[3] = 2.0
hval[4] = 2.0
hval[5] = 4.0
return 0
end
# Dense Hessian
function hess_dense(n::Int, ne::Int, x::Vector{T}, hval::Vector{T},
userdata::userdata_arc)
hval[1] = 2.0 - cos(x[1])
hval[2] = 0.0
hval[3] = 2.0
hval[4] = 2.0
hval[5] = 2.0
hval[6] = 4.0
return 0
end
# Hessian-vector product
function hessprod(n::Int, x::Vector{T}, u::Vector{T}, v::Vector{T},
got_h::Bool, userdata::userdata_arc)
u[1] = u[1] + 2.0 * (v[1] + v[3]) - cos(x[1]) * v[1]
u[2] = u[2] + 2.0 * (v[2] + v[3])
u[3] = u[3] + 2.0 * (v[1] + v[2] + 2.0 * v[3])
return 0
end
# Apply preconditioner
function prec(n::Int, x::Vector{T}, u::Vector{T}, v::Vector{T},
userdata::userdata_arc)
u[1] = 0.5 * v[1]
u[2] = 0.5 * v[2]
u[3] = 0.25 * v[3]
return 0
end
# Objective function
function fun_diag(n::Int, x::Vector{T}, f::Ref{T}, userdata::userdata_arc)
p = userdata.p
f[] = (x[3] + p)^2 + x[2]^2 + cos(x[1])
return 0
end
# Gradient of the objective
function grad_diag(n::Int, x::Vector{T}, g::Vector{T}, userdata::userdata_arc)
p = userdata.p
g[1] = -sin(x[1])
g[2] = 2.0 * x[2]
g[3] = 2.0 * (x[3] + p)
return 0
end
# Hessian of the objective
function hess_diag(n::Int, ne::Int, x::Vector{T}, hval::Vector{T},
userdata::userdata_arc)
hval[1] = -cos(x[1])
hval[2] = 2.0
hval[3] = 2.0
return 0
end
# Hessian-vector product
function hessprod_diag(n::Int, x::Vector{T}, u::Vector{T}, v::Vector{T},
got_h::Bool, userdata::userdata_arc)
u[1] = u[1] + -cos(x[1]) * v[1]
u[2] = u[2] + 2.0 * v[2]
u[3] = u[3] + 2.0 * v[3]
return 0
end
# Derived types
data = Ref{Ptr{Cvoid}}()
control = Ref{arc_control_type{T}}()
inform = Ref{arc_inform_type{T}}()
# Set user data
userdata = userdata_arc(4.0)
# Set problem data
n = 3 # dimension
ne = 5 # Hesssian elements
H_row = Cint[1, 2, 3, 3, 3] # Hessian H
H_col = Cint[1, 2, 1, 2, 3] # NB lower triangle
H_ptr = Cint[1, 2, 3, 6] # row pointers
# Set storage
g = zeros(T, n) # gradient
st = ' '
status = Ref{Cint}()
@printf(" Fortran sparse matrix indexing\n\n")
@printf(" tests reverse-communication options\n\n")
# reverse-communication input/output
eval_status = Ref{Cint}()
f = Ref{T}(0.0)
u = zeros(T, n)
v = zeros(T, n)
H_val = zeros(T, ne)
H_dense = zeros(T, div(n * (n + 1), 2))
H_diag = zeros(T, n)
for d in 1:5
# Initialize ARC
arc_initialize(T, data, control, status)
# Set user-defined control options
@reset control[].f_indexing = true # Fortran sparse matrix indexing
# @reset control[].print_level = Cint(1)
# Start from 1.5
x = T[1.5, 1.5, 1.5]
# sparse co-ordinate storage
if d == 1
st = 'C'
arc_import(T, control, data, status, n, "coordinate",
ne, H_row, H_col, C_NULL)
terminated = false
while !terminated # reverse-communication loop
arc_solve_reverse_with_mat(T, data, status, eval_status, n, x, f[], g, ne, H_val, u, v)
if status[] == 0 # successful termination
terminated = true
elseif status[] < 0 # error exit
terminated = true
elseif status[] == 2 # evaluate f
eval_status[] = fun(n, x, f, userdata)
elseif status[] == 3 # evaluate g
eval_status[] = grad(n, x, g, userdata)
elseif status[] == 4 # evaluate H
eval_status[] = hess(n, ne, x, H_val, userdata)
elseif status[] == 6 # evaluate the product with P
eval_status[] = prec(n, x, u, v, userdata)
else
@printf(" the value %1i of status should not occur\n", status)
end
end
end
# sparse by rows
if d == 2
st = 'R'
arc_import(T, control, data, status, n, "sparse_by_rows", ne,
C_NULL, H_col, H_ptr)
terminated = false
while !terminated # reverse-communication loop
arc_solve_reverse_with_mat(T, data, status, eval_status,
n, x, f[], g, ne, H_val, u, v)
if status[] == 0 # successful termination
terminated = true
elseif status[] < 0 # error exit
terminated = true
elseif status[] == 2 # evaluate f
eval_status[] = fun(n, x, f, userdata)
elseif status[] == 3 # evaluate g
eval_status[] = grad(n, x, g, userdata)
elseif status[] == 4 # evaluate H
eval_status[] = hess(n, ne, x, H_val, userdata)
elseif status[] == 6 # evaluate the product with P
eval_status[] = prec(n, x, u, v, userdata)
else
@printf(" the value %1i of status should not occur\n", status)
end
end
end
# dense
if d == 3
st = 'D'
arc_import(T, control, data, status, n, "dense",
ne, C_NULL, C_NULL, C_NULL)
terminated = false
while !terminated # reverse-communication loop
arc_solve_reverse_with_mat(T, data, status, eval_status,
n, x, f[], g, div(n * (n + 1), 2), H_dense, u, v)
if status[] == 0 # successful termination
terminated = true
elseif status[] < 0 # error exit
terminated = true
elseif status[] == 2 # evaluate f
eval_status[] = fun(n, x, f, userdata)
elseif status[] == 3 # evaluate g
eval_status[] = grad(n, x, g, userdata)
elseif status[] == 4 # evaluate H
eval_status[] = hess_dense(n, div(n * (n + 1), 2), x, H_dense, userdata)
elseif status[] == 6 # evaluate the product with P
eval_status[] = prec(n, x, u, v, userdata)
else
@printf(" the value %1i of status should not occur\n", status)
end
end
end
# diagonal
if d == 4
st = 'I'
arc_import(T, control, data, status, n, "diagonal",
ne, C_NULL, C_NULL, C_NULL)
terminated = false
while !terminated # reverse-communication loop
arc_solve_reverse_with_mat(T, data, status, eval_status,
n, x, f[], g, n, H_diag, u, v)
if status[] == 0 # successful termination
terminated = true
elseif status[] < 0 # error exit
terminated = true
elseif status[] == 2 # evaluate f
eval_status[] = fun_diag(n, x, f, userdata)
elseif status[] == 3 # evaluate g
eval_status[] = grad_diag(n, x, g, userdata)
elseif status[] == 4 # evaluate H
eval_status[] = hess_diag(n, n, x, H_diag, userdata)
elseif status[] == 6 # evaluate the product with P
eval_status[] = prec(n, x, u, v, userdata)
else
@printf(" the value %1i of status should not occur\n", status)
end
end
end
# access by products
if d == 5
st = 'P'
arc_import(T, control, data, status, n, "absent",
ne, C_NULL, C_NULL, C_NULL)
terminated = false
while !terminated # reverse-communication loop
arc_solve_reverse_without_mat(T, data, status, eval_status,
n, x, f[], g, u, v)
if status[] == 0 # successful termination
terminated = true
elseif status[] < 0 # error exit
terminated = true
elseif status[] == 2 # evaluate f
eval_status[] = fun(n, x, f, userdata)
elseif status[] == 3 # evaluate g
eval_status[] = grad(n, x, g, userdata)
elseif status[] == 5 # evaluate H
eval_status[] = hessprod(n, x, u, v, false, userdata)
elseif status[] == 6 # evaluate the product with P
eval_status[] = prec(n, x, u, v, userdata)
else
@printf(" the value %1i of status should not occur\n", status)
end
end
end
arc_information(T, data, inform, status)
if inform[].status[] == 0
@printf("%c:%6i iterations. Optimal objective value = %5.2f status = %1i\n", st,
inform[].iter, inform[].obj, inform[].status)
else
@printf("%c: ARC_solve exit status = %1i\n", st, inform[].status)
end
# @printf("x: ")
# for i = 1:n
# @printf("%f ", x[i])
# end
# @printf("\n")
# @printf("gradient: ")
# for i = 1:n
# @printf("%f ", g[i])
# end
# @printf("\n")
# Delete internal workspace
arc_terminate(T, data, control, inform)
end
return 0
end
@testset "ARC" begin
@test test_arc(Float32) == 0
@test test_arc(Float64) == 0
end