snls_control_type structure#
#include <galahad_snls.h> struct snls_control_type { // components bool f_indexing; ipc_ error; ipc_ out; ipc_ print_level; ipc_ start_print; ipc_ stop_print; ipc_ print_gap; ipc_ maxit; ipc_ alive_unit; char alive_file[31]; ipc_ jacobian_available; ipc_ subproblem_solver; ipc_ non_monotone; ipc_ weight_update_strategy; rpc_ stop_r_absolute; rpc_ stop_r_relative; rpc_ stop_pg_absolute; rpc_ stop_pg_relative; rpc_ stop_s; rpc_ stop_pg_switch; rpc_ initial_weight; rpc_ minimum_weight; rpc_ eta_successful; rpc_ eta_very_successful; rpc_ eta_too_successful; rpc_ weight_decrease_min; rpc_ weight_decrease; rpc_ weight_increase; rpc_ weight_increase_max; rpc_ switch_to_newton; rpc_ cpu_time_limit; rpc_ clock_time_limit; bool newton_acceleration; bool magic_step; bool print_obj; bool space_critical; bool deallocate_error_fatal; char prefix[31]; struct slls_control_type slls_control; struct sllsb_control_type sllsb_control; };
detailed documentation#
control derived type as a C struct
components#
bool f_indexing
use C or Fortran sparse matrix indexing
ipc_ error
error and warning diagnostics occur on stream error
ipc_ out
general output occurs on stream out
ipc_ 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
ipc_ start_print
any printing will start on this iteration
ipc_ stop_print
any printing will stop on this iteration
ipc_ print_gap
the number of iterations between printing
ipc_ maxit
the maximum number of iterations performed
ipc_ alive_unit
removal of the file alive_file from unit alive_unit terminates execution
char alive_file[31]
see alive_unit
ipc_ jacobian_available
is the Jacobian matrix of first derivatives available (\(\geq\) 2), is access only via matrix-vector products (=1) or is it not available (\(\leq\) 0) ?
ipc_ subproblem_solver
the method used to solve the crucial step-determination subproblem. Possible values are
1 a projected-gradient method using GALAHAD’s
sllswill be used2 an interior-point method using GALAHAD’s
sllsbwill be used3 an interior-point method will initially be used, but a switch to a projected-gradient method will occur when sufficient progress has occurred (see .stop_pg_switch).
ipc_ non_monotone
non-monotone \(\leq\) 0 monotone strategy used, anything else non-monotone strategy with this history length used
ipc_ weight_update_strategy
define the weight-update strategy: 1 (basic), 2 (reset to zero when very successful), 3 (imitate TR), 4 (increase lower bound), 5 (GPT)
rpc_ stop_r_absolute
overall convergence tolerances. The iteration will terminate when \(||r(x)||_2 \leq\) MAX( .stop_r_absolute, .stop_r_relative \(* \|r(x_0)\|_2\) or when the norm of the gradient, \(g(x) = J^T(x) W r(x)\) satisfies \(\|P[x-g(x)]-x\|_2 \leq\) MAX( .stop_pg_absolute, .stop_pg_relative \(* \|P[x_0-g(x_0)]-x_0\|_2\) or if the norm of step is less than .stop_s, where \(x_0\) is the initial point.
rpc_ stop_r_relative
see stop_r_absolute
rpc_ stop_pg_absolute
see stop_r_absolute
rpc_ stop_pg_relative
see stop_r_absolute
rpc_ stop_s
see stop_r_absolute
rpc_ stop_pg_switch
the step-computation solver will switch from an interior-point method to a projected-gradient one if .subproblem_solver = 3 (see above) and \(\|P[x-g(x)]-x\|_2 \leq\) MAX( .stop_pg_absolute, .stop_pg_switch $* \|P[x_0-g(x_0)]-x_0\|_2.
rpc_ initial_weight
initial value for the regularization weight (-ve => \(1/\|g_0\|)\))
rpc_ minimum_weight
minimum permitted regularization weight
rpc_ 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 decreaed if this relative decrease is greater than .eta_very_successful but smaller than .eta_too_successful
rpc_ eta_very_successful
see eta_successful
rpc_ eta_too_successful
see eta_successful
rpc_ 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 unsucceful, 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)
rpc_ weight_decrease
see weight_decrease_min
rpc_ weight_increase
see weight_decrease_min
rpc_ weight_increase_max
see weight_decrease_min
rpc_ switch_to_newton
if the value of the two-norm of the projected gradient is less than .switch_to_newton, a switch is made from the Gauss-Newton model to the Newton one when .newton_acceleration is true
rpc_ cpu_time_limit
the maximum CPU time allowed (-ve means infinite)
rpc_ clock_time_limit
the maximum elapsed clock time allowed (-ve means infinite)
bool newton_acceleration
if they are available, second derivatives should be used to accelerate the convergence of the algorithm
bool magic_step
allow the user to perform a “magic” step to improve the objective
bool print_obj
print values of the objective/gradient rather than \(\|r\|\) and its gradient
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
char prefix[31]
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 slls_control_type slls_control
control parameters for SLLS
struct sllsb_control_type sllsb_control
control parameters for SLLSB