Process Object

This object specifies an AO process.

Active bindings

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.

Methods

Public methods

• Process$new()

• Process$print_status()

• Process$initialize_details()

• Process$update_details()

• Process$get_partition()

• Process$get_details()

• Process$get_value()

• Process$get_value_latest()

• Process$get_value_best()

• Process$get_parameter()

• Process$get_parameter_latest()

• Process$get_parameter_best()

• Process$get_seconds()

• Process$get_seconds_total()

• Process$check_stopping()

---

Method new()

Creates a new object of this R6 class.

Usage

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
)

Arguments

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?

---

Method print_status()

Prints a status message.

Usage

Process$print_status(message, message_type = 8, verbose = self$verbose)

Arguments

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?

---

Method initialize_details()

Initializes the details part of the output.

Usage

Process$initialize_details(initial_parameter, initial_value)

Arguments

initial_parameter

[numeric()]
The starting parameter values for the AO process.

initial_value

[numeric(1)]
The function value at the initial parameters.

---

Method update_details()

Updates the details part of the output.

Usage

Process$update_details(
  value,
  parameter_block,
  seconds,
  error,
  error_message,
  block = self$block
)

Arguments

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.

---

Method get_partition()

Get a parameter partition.

Usage

Process$get_partition()

---

Method get_details()

Get the details part of the output.

Usage

Process$get_details(
  which_iteration = NULL,
  which_block = NULL,
  which_column = c("iteration", "value", "parameter", "block", "seconds")
)

Arguments

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".

---

Method get_value()

Get the function value in different steps of the AO process.

Usage

Process$get_value(
  which_iteration = NULL,
  which_block = NULL,
  keep_iteration_column = FALSE,
  keep_block_columns = FALSE
)

Arguments

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?

---

Method get_value_latest()

Get the function value in the latest step of the AO process.

Usage

Process$get_value_latest()

---

Method get_value_best()

Get the optimum function value in the AO process.

Usage

Process$get_value_best()

---

Method get_parameter()

Get the parameter values in different steps of the AO process.

Usage

Process$get_parameter(
  which_iteration = self$iteration,
  which_block = NULL,
  keep_iteration_column = FALSE,
  keep_block_columns = FALSE
)

Arguments

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?

---

Method get_parameter_latest()

Get the parameter value in the latest step of the AO process.

Usage

Process$get_parameter_latest(parameter_type = "full")

Arguments

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

---

Method get_parameter_best()

Get the optimum parameter value in the AO process.

Usage

Process$get_parameter_best(parameter_type = "full")

Arguments

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

---

Method get_seconds()

Get the optimization time in seconds in different steps of the AO process.

Usage

Process$get_seconds(
  which_iteration = NULL,
  which_block = NULL,
  keep_iteration_column = FALSE,
  keep_block_columns = FALSE
)

Arguments

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?

---

Method get_seconds_total()

Get the total optimization time in seconds of the AO process.

Usage

Process$get_seconds_total()

---

Method check_stopping()

Checks if the AO process can be terminated.

Usage

Process$check_stopping()