This object specifies an AO process.
npar[integer(1)]
The (total) length of the target argument(s).
partition[character(1) | list()]
Defines the parameter partition, and can be either
"sequential" for treating each parameter separately,
"random" for a random partition in each iteration,
"none" for no partition (which is equivalent to joint optimization),
or a list of vectors of parameter indices, specifying a custom
partition for the AO process.
new_block_probability[numeric(1)]
Only relevant if partition = "random".
The probability for a new parameter block when creating a random partition.
Values close to 0 result in larger parameter blocks, values close to 1 result in smaller parameter blocks.
minimum_block_number[integer(1)]
Only relevant if partition = "random".
The minimum number of blocks in random partitions.
verbose[logical(1)]
Print tracing details during the AO process?
minimize[logical(1)]
Minimize during the AO process?
If FALSE, maximization is performed.
iteration_limit[integer(1) | Inf]
The maximum number of iterations through the parameter partition before
the AO process is terminated.
Can also be Inf for no iteration limit.
seconds_limit[numeric(1)]
The time limit in seconds before the AO process is terminated.
Can also be Inf for no time limit.
Note that this stopping criteria is only checked after a sub-problem is solved and not within solving a sub-problem, so the actual process time can exceed this limit.
tolerance_value[numeric(1)]
A non-negative tolerance value. The AO process terminates
if the absolute difference between the current function value and the one
before tolerance_history iterations is smaller than
tolerance_value.
Can be 0 for no value threshold.
tolerance_parameter[numeric(1)]
A non-negative tolerance value. The AO process terminates if
the distance between the current estimate and the before
tolerance_history iterations is smaller than
tolerance_parameter.
Can be 0 for no parameter threshold.
By default, the distance is measured using the euclidean norm, but another
norm can be specified via the tolerance_parameter_norm field.
tolerance_parameter_norm[function]
The norm that measures the distance between the current estimate and the
one from the last iteration. If the distance is smaller than
tolerance_parameter, the AO process is terminated.
It must be of the form function(x, y) for two vector inputs
x and y, and return a single numeric value.
By default, the euclidean norm function(x, y) sqrt(sum((x - y)^2))
is used.
tolerance_history[integer(1)]
The number of iterations to look back to determine whether
tolerance_value or tolerance_parameter has been reached.
add_details[logical(1)]
Add details about the AO process to the output?
iteration[integer(1)]
The current iteration number.
block[integer()]
The currently active parameter block, represented as parameter indices.
output[list(), read-only]
The output of the AO process, which is a list with the following
elements:
estimate is the parameter vector at termination.
value is the function value at termination.
details is a data.frame with full information about the AO process.
For each iteration (column iteration) it contains the function value
(column value), parameter values (columns starting with p followed by
the parameter index), the active parameter block (columns starting with b
followed by the parameter index, where 1 stands for a parameter contained
in the active parameter block and 0 if not), and computation times in seconds
(column seconds). Only available if add_details = TRUE.
seconds is the overall computation time in seconds.
stopping_reason is a message why the AO process has terminated.
new()Creates a new object of this R6 class.
Process$new(
npar = integer(),
partition = "sequential",
new_block_probability = 0.3,
minimum_block_number = 1,
verbose = FALSE,
minimize = TRUE,
iteration_limit = Inf,
seconds_limit = Inf,
tolerance_value = 1e-06,
tolerance_parameter = 1e-06,
tolerance_parameter_norm = function(x, y) sqrt(sum((x - y)^2)),
tolerance_history = 1,
add_details = TRUE
)npar[integer(1)]
The (total) length of the target argument(s).
partition[character(1) | list()]
Defines the parameter partition, and can be either
"sequential" for treating each parameter separately,
"random" for a random partition in each iteration,
"none" for no partition (which is equivalent to joint optimization),
or a list of vectors of parameter indices, specifying a custom
partition for the AO process.
new_block_probability[numeric(1)]
Only relevant if partition = "random".
The probability for a new parameter block when creating a random partition.
Values close to 0 result in larger parameter blocks, values close to 1 result in smaller parameter blocks.
minimum_block_number[integer(1)]
Only relevant if partition = "random".
The minimum number of blocks in random partitions.
verbose[logical(1)]
Print tracing details during the AO process?
minimize[logical(1)]
Minimize during the AO process?
If FALSE, maximization is performed.
iteration_limit[integer(1) | Inf]
The maximum number of iterations through the parameter partition before
the AO process is terminated.
Can also be Inf for no iteration limit.
seconds_limit[numeric(1)]
The time limit in seconds before the AO process is terminated.
Can also be Inf for no time limit.
Note that this stopping criteria is only checked after a sub-problem is solved and not within solving a sub-problem, so the actual process time can exceed this limit.
tolerance_value[numeric(1)]
A non-negative tolerance value. The AO process terminates
if the absolute difference between the current function value and the one
before tolerance_history iterations is smaller than
tolerance_value.
Can be 0 for no value threshold.
tolerance_parameter[numeric(1)]
A non-negative tolerance value. The AO process terminates if
the distance between the current estimate and the before
tolerance_history iterations is smaller than
tolerance_parameter.
Can be 0 for no parameter threshold.
By default, the distance is measured using the euclidean norm, but another
norm can be specified via the tolerance_parameter_norm field.
tolerance_parameter_norm[function]
The norm that measures the distance between the current estimate and the
one from the last iteration. If the distance is smaller than
tolerance_parameter, the AO process is terminated.
It must be of the form function(x, y) for two vector inputs
x and y, and return a single numeric value.
By default, the euclidean norm function(x, y) sqrt(sum((x - y)^2))
is used.
tolerance_history[integer(1)]
The number of iterations to look back to determine whether
tolerance_value or tolerance_parameter has been reached.
add_details[logical(1)]
Add details about the AO process to the output?
print_status()Prints a status message.
Process$print_status(message, message_type = 8, verbose = self$verbose)message[character(1)]
A status message.
message_type[integer(1)]
The message type, one of the following:
1 for cli::cli_h1()
2 for cli::cli_h2()
3 for cli::cli_h3()
4 for cli::cli_alert_success()
5 for cli::cli_alert_info()
6 for cli::cli_alert_warning()
7 for cli::cli_alert_danger()
8 for cli::cat_line()
verbose[logical(1)]
Print tracing details during the AO process?
initialize_details()Initializes the details part of the output.
Process$initialize_details(initial_parameter, initial_value)initial_parameter[numeric()]
The starting parameter values for the AO process.
initial_value[numeric(1)]
The function value at the initial parameters.
update_details()Updates the details part of the output.
Process$update_details(
value,
parameter_block,
seconds,
error,
error_message,
block = self$block
)value[numeric(1)]
The updated function value.
parameter_block[numeric()]
The updated parameter values for the active parameter block.
seconds[numeric(1)]
The time in seconds for solving the sub-problem.
error[logical(1)]
Did solving the sub-problem result in an error?
error_message[character(1)]
An error message if error = TRUE.
block[integer()]
The currently active parameter block, represented as parameter indices.
get_partition()Get a parameter partition.
Process$get_partition()
get_details()Get the details part of the output.
Process$get_details(
which_iteration = NULL,
which_block = NULL,
which_column = c("iteration", "value", "parameter", "block", "seconds")
)which_iteration[integer()]
Selects the iteration(s).
Can also be NULL to select all iterations.
which_block[character(1) | integer()]
Selects the parameter block in the partition and can be one of
"first" for the first parameter block,
"last" for the last parameter block,
an integer vector of parameter indices,
or NULL for all parameter blocks.
which_column[character()]
Selects the columns in the details part of the output and can be one or
more of
"iteration",
"value",
"parameter",
"block",
and "seconds".
get_value()Get the function value in different steps of the AO process.
Process$get_value(
which_iteration = NULL,
which_block = NULL,
keep_iteration_column = FALSE,
keep_block_columns = FALSE
)which_iteration[integer()]
Selects the iteration(s).
Can also be NULL to select all iterations.
which_block[character(1) | integer()]
Selects the parameter block in the partition and can be one of
"first" for the first parameter block,
"last" for the last parameter block,
an integer vector of parameter indices,
or NULL for all parameter blocks.
keep_iteration_column[logical(1)]
Keep the column containing the information about the iteration in the output?
keep_block_columns[logical(1)]
Keep the column containing the information about the active parameter block
in the output?
get_value_latest()Get the function value in the latest step of the AO process.
Process$get_value_latest()
get_value_best()Get the optimum function value in the AO process.
Process$get_value_best()
get_parameter()Get the parameter values in different steps of the AO process.
Process$get_parameter(
which_iteration = self$iteration,
which_block = NULL,
keep_iteration_column = FALSE,
keep_block_columns = FALSE
)which_iteration[integer()]
Selects the iteration(s).
Can also be NULL to select all iterations.
which_block[character(1) | integer()]
Selects the parameter block in the partition and can be one of
"first" for the first parameter block,
"last" for the last parameter block,
an integer vector of parameter indices,
or NULL for all parameter blocks.
keep_iteration_column[logical(1)]
Keep the column containing the information about the iteration in the output?
keep_block_columns[logical(1)]
Keep the column containing the information about the active parameter block
in the output?
get_parameter_latest()Get the parameter value in the latest step of the AO process.
Process$get_parameter_latest(parameter_type = "full")parameter_type[character(1)]
Selects the parameter type and can be one of
"full" (default) to get the full parameter vector,
"block" to get the parameter values for the current block,
i.e., the parameters with the indices self$block
"fixed" to get the parameter values which are currently fixed,
i.e., all except for those with the indices self$block
get_parameter_best()Get the optimum parameter value in the AO process.
Process$get_parameter_best(parameter_type = "full")parameter_type[character(1)]
Selects the parameter type and can be one of
"full" (default) to get the full parameter vector,
"block" to get the parameter values for the current block,
i.e., the parameters with the indices self$block
"fixed" to get the parameter values which are currently fixed,
i.e., all except for those with the indices self$block
get_seconds()Get the optimization time in seconds in different steps of the AO process.
Process$get_seconds(
which_iteration = NULL,
which_block = NULL,
keep_iteration_column = FALSE,
keep_block_columns = FALSE
)which_iteration[integer()]
Selects the iteration(s).
Can also be NULL to select all iterations.
which_block[character(1) | integer()]
Selects the parameter block in the partition and can be one of
"first" for the first parameter block,
"last" for the last parameter block,
an integer vector of parameter indices,
or NULL for all parameter blocks.
keep_iteration_column[logical(1)]
Keep the column containing the information about the iteration in the output?
keep_block_columns[logical(1)]
Keep the column containing the information about the active parameter block
in the output?
get_seconds_total()Get the total optimization time in seconds of the AO process.
Process$get_seconds_total()
check_stopping()Checks if the AO process can be terminated.
Process$check_stopping()