Procedure {ao} | R Documentation |
This object specifies alternating optimization procedure.
npar
(integer(1)
)
The length of the target argument.
partition
(character(1)
or 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 alternating optimization process.
new_block_probability
(numeric(1)
)
Only relevant if partition = "random"
.
The probability for a new parameter block when creating a random
partitions.
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)
)
Whether to print tracing details during the alternating optimization
process.
minimize
(logical(1)
)
Whether to minimize during the alternating optimization process.
If FALSE
, maximization is performed.
iteration_limit
(integer(1)
or Inf
)
The maximum number of iterations through the parameter partition before
the alternating optimization process is terminated.
Can also be Inf
for no iteration limit.
seconds_limit
(numeric(1)
)
The time limit in seconds before the alternating optimization 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 alternating optimization 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 alternating optimization 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 procedure 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.
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 alternating optimization procedure, 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 procedure:
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
)
seconds
is the overall computation time in seconds.
stopping_reason
is a message why the procedure has terminated.
new()
Creates a new object of this R6 class.
Procedure$new( npar = integer(), partition = "sequential", new_block_probability = 0.3, minimum_block_number = 2, 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 )
npar
(integer(1)
)
The (total) length of the target argument(s).
partition
(character(1)
or 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 alternating optimization process.
new_block_probability
(numeric(1)
)
Only relevant if partition = "random"
.
The probability for a new parameter block when creating a random
partitions.
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)
)
Whether to print tracing details during the alternating optimization
process.
minimize
(logical(1)
)
Whether to minimize during the alternating optimization process.
If FALSE
, maximization is performed.
iteration_limit
(integer(1)
or Inf
)
The maximum number of iterations through the parameter partition before
the alternating optimization process is terminated.
Can also be Inf
for no iteration limit.
seconds_limit
(numeric(1)
)
The time limit in seconds before the alternating optimization 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 alternating optimization 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 alternating optimization 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 procedure 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.
print_status()
Prints a status message.
Procedure$print_status(message, message_type = 8, verbose = self$verbose)
message
(character(1)
)
The status message.
message_type
(integer(1)
)
The type of the message, one of the following:
1
for cli::cli_h1()
2
for cli::cli_h2()
3
for cli::cli_h3()
4
for cli::cli_alert_success()
5
for cli::cli_alert_info()
6
for cli::cli_alert_warning()
7
for cli::cli_alert_danger()
8
for cli::cat_line()
verbose
(logical(1)
)
Whether to print tracing details during the alternating optimization
process.
initialize_details()
Initializes the details
part of the output.
Procedure$initialize_details(initial_parameter, initial_value)
initial_parameter
(numeric()
)
The starting parameter values for the procedure.
initial_value
(numeric(1)
)
The function value at the initial parameters.
update_details()
Updates the details
part of the output.
Procedure$update_details( value, parameter_block, seconds, error, block = self$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)
)
Whether solving the sub-problem resulted in an error.
block
(integer()
)
The currently active parameter block, represented as parameter indices.
get_partition()
Get a parameter partition.
Procedure$get_partition()
get_details()
Get the details
part of the output.
Procedure$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)
or 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 alternating optimization procedure.
Procedure$get_value( which_iteration = NULL, which_block = NULL, keep_iteration_column = FALSE, keep_block_columns = FALSE )
which_iteration
(integer()
)
Selects the iteration(s).
Can also be NULL
to select all iterations.
which_block
(character(1)
or integer()
)
Selects the parameter block in the partition and can be one of
"first"
for the first parameter block,
"last"
for the last parameter block,
an integer
vector of parameter indices,
or NULL
for all parameter blocks.
keep_iteration_column
(logical()
)
Whether to keep the column containing the information about the iteration
in the output.
keep_block_columns
(logical()
)
Whether to keep the column containing the information about the active
parameter block in the output.
get_value_latest()
Get the function value in the latest step of the alternating optimization procedure.
Procedure$get_value_latest()
get_value_best()
Get the optimum function value in the alternating optimization procedure.
Procedure$get_value_best()
get_parameter()
Get the parameter values in different steps of the alternating optimization procedure.
Procedure$get_parameter( which_iteration = self$iteration, which_block = NULL, keep_iteration_column = FALSE, keep_block_columns = FALSE )
which_iteration
(integer()
)
Selects the iteration(s).
Can also be NULL
to select all iterations.
which_block
(character(1)
or integer()
)
Selects the parameter block in the partition and can be one of
"first"
for the first parameter block,
"last"
for the last parameter block,
an integer
vector of parameter indices,
or NULL
for all parameter blocks.
keep_iteration_column
(logical()
)
Whether to keep the column containing the information about the iteration
in the output.
keep_block_columns
(logical()
)
Whether to keep the column containing the information about the active
parameter block in the output.
get_parameter_latest()
Get the parameter value in the latest step of the alternating optimization procedure.
Procedure$get_parameter_latest(parameter_type = "full")
parameter_type
(character(1)
)
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 alternating optimization procedure.
Procedure$get_parameter_best(parameter_type = "full")
parameter_type
(character(1)
)
Can be one of
"full"
(default) to get the full parameter vector,
"block"
to get the parameter values for the current block,
i.e., the parameters with the indices self$block
"fixed"
to get the parameter values which are currently fixed,
i.e., all except for those with the indices self$block
get_seconds()
Get the optimization time in seconds in different steps of the alternating optimization procedure.
Procedure$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)
or 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()
)
Whether to keep the column containing the information about the iteration
in the output.
keep_block_columns
(logical()
)
Whether to 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 alternating optimization procedure.
Procedure$get_seconds_total()
check_stopping()
Checks if the alternating optimization procedure can be terminated.
Procedure$check_stopping()