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Specify numerical optimizer object

Source: R/optimizer.R
Optimizer.Rd

The Optimizer object defines a numerical optimizer based on any optimization algorithm implemented in R. The main advantage of working with an Optimizer object instead of using the optimization function directly lies in the standardized inputs and outputs.

Any R function that fulfills the following four constraints can be defined as an Optimizer object:

  1. It must have an input for a function, the objective function to be optimized.

  2. It must have an input for a numeric vector, the initial values where the optimizer starts.

  3. It must have a ... argument for additional parameters passed on to the objective function.

  4. The output must be a named list, including the optimal function value and the optimal parameter vector.

Active bindings

label

[character(1)]
The label for the optimizer.

algorithm

[function]
The optimization algorithm.

arg_objective

[character(1)]
The argument name for the objective function in algorithm.

arg_initial

[character(1)]
The argument name for the initial values in algorithm.

arg_lower

[character(1) | NA]
Optionally the argument name for the lower parameter bound in algorithm.

Can be NA if not available.

arg_upper

[character(1) | NA]
Optionally the argument name for the upper parameter bound in algorithm.

Can be NA if not available.

arg_gradient

[character(1) | NA]
Optionally the argument name for the gradient function in algorithm.

Can be NA if not available.

arg_hessian

[character(1) | NA]
Optionally the argument name for the Hessian function in algorithm.

Can be NA if not available.

gradient_as_attribute

[logical(1)]
Only relevant if arg_gradient is not NA.

In that case, does algorithm expect that the gradient is an attribute of the objective function output (as for example in nlm)? In that case, arg_gradient defines the attribute name.

hessian_as_attribute

[logical(1)]
Only relevant if arg_hessian is not NA.

In that case, does algorithm expect that the Hessian is an attribute of the objective function output (as for example in nlm)? In that case, arg_hessian defines the attribute name.

out_value

[character(1)]
The element name for the optimal function value in the output list of algorithm.

out_parameter

[character(1)]
The element name for the optimal parameters in the output list of algorithm.

direction

[character(1)]
Either "min" (if the optimizer minimizes) or "max" (if the optimizer maximizes).

arguments

[list()]
Custom arguments for algorithm.

Defaults are used for arguments that are not specified.

seconds

[numeric(1)]
A time limit in seconds.

Optimization is interrupted prematurely if seconds is exceeded.

No time limit if seconds = Inf (the default).

Note the limitations documented in setTimeLimit.

hide_warnings

[logical(1)]
Hide warnings during optimization?

output_ignore

[character()]
Elements to ignore (not include) in the optimization output.

Methods

Public methods

Method new()

Initializes a new Optimizer object.

Usage

Optimizer$new(which, ..., .verbose = TRUE)

Arguments

which

[character(1)]
Either:

  • one of optimizer_dictionary$keys

  • or "custom", in which case $definition() must be used to define the optimizer details.

...

[any]
In Optimizer$new() and $set_arguments(), named arguments to pass to the optimizer algorithm. In $minimize(), $maximize(), and $optimize(), named arguments to pass to the objective function.

.verbose

[logical(1)]
Print status messages?

Returns

A new Optimizer object.

Method definition()

Defines an optimizer.

Usage

Optimizer$definition(
  algorithm,
  arg_objective,
  arg_initial,
  arg_lower = NA,
  arg_upper = NA,
  arg_gradient = NA,
  arg_hessian = NA,
  gradient_as_attribute = FALSE,
  hessian_as_attribute = FALSE,
  out_value,
  out_parameter,
  direction
)

Arguments

algorithm

[function]
The optimization algorithm.

arg_objective

[character(1)]
The argument name for the objective function in algorithm.

arg_initial

[character(1)]
The argument name for the initial values in algorithm.

arg_lower

[character(1) | NA]
Optionally the argument name for the lower parameter bound in algorithm.

Can be NA if not available.

arg_upper

[character(1) | NA]
Optionally the argument name for the upper parameter bound in algorithm.

Can be NA if not available.

arg_gradient

[character(1) | NA]
Optionally the argument name for the gradient function in algorithm.

Can be NA if not available.

arg_hessian

[character(1) | NA]
Optionally the argument name for the Hessian function in algorithm.

Can be NA if not available.

gradient_as_attribute

[logical(1)]
Only relevant if arg_gradient is not NA.

In that case, does algorithm expect that the gradient is an attribute of the objective function output (as for example in nlm)? In that case, arg_gradient defines the attribute name.

hessian_as_attribute

[logical(1)]
Only relevant if arg_hessian is not NA.

In that case, does algorithm expect that the Hessian is an attribute of the objective function output (as for example in nlm)? In that case, arg_hessian defines the attribute name.

out_value

[character(1)]
The element name for the optimal function value in the output list of algorithm.

out_parameter

[character(1)]
The element name for the optimal parameters in the output list of algorithm.

direction

[character(1)]
Either "min" (if the optimizer minimizes) or "max" (if the optimizer maximizes).

Method set_arguments()

Sets optimizer arguments.

Usage

Optimizer$set_arguments(...)

Arguments

...

[any]
In Optimizer$new() and $set_arguments(), named arguments to pass to the optimizer algorithm. In $minimize(), $maximize(), and $optimize(), named arguments to pass to the objective function.

Returns

The Optimizer object.

Method minimize()

Perform minimization.

Usage

Optimizer$minimize(objective, initial, lower = NA, upper = NA, ...)

Arguments

objective

[function | Objective]
A function to be optimized that

  1. has at least one argument that receives a numeric vector

  2. and returns a single numeric value.

Alternatively, it can also be an Objective object for more flexibility.

initial

[numeric()]
Starting parameter values for the optimization.

lower

[NA | numeric() | numeric(1)]
Lower bounds on the parameters.

If a single number, this will be applied to all parameters.

Can be NA to not define any bounds.

upper

[NA | numeric() | numeric(1)]
Upper bounds on the parameters.

If a single number, this will be applied to all parameters.

Can be NA to not define any bounds.

...

[any]
In Optimizer$new() and $set_arguments(), named arguments to pass to the optimizer algorithm. In $minimize(), $maximize(), and $optimize(), named arguments to pass to the objective function.

Returns

A named list, containing at least these five elements:

value

A numeric, the minimum function value.

parameter

A numeric vector, the parameter vector where the minimum is obtained.

seconds

A numeric, the optimization time in seconds.

initial

A numeric, the initial parameter values.

error

A logical flag: TRUE if an error occurred, otherwise FALSE.

Additional output elements from the optimizer are appended.

If an error occurred, the error message is also appended as element error_message.

If the time limit was exceeded, this counts as an error. In addition, the flag time_out = TRUE is appended.

Examples

Optimizer$new("stats::nlm")$
  minimize(objective = function(x) x^4 + 3*x - 5, initial = 2)

Method maximize()

Perform maximization.

Usage

Optimizer$maximize(objective, initial, lower = NA, upper = NA, ...)

Arguments

objective

[function | Objective]
A function to be optimized that

  1. has at least one argument that receives a numeric vector

  2. and returns a single numeric value.

Alternatively, it can also be an Objective object for more flexibility.

initial

[numeric()]
Starting parameter values for the optimization.

lower

[NA | numeric() | numeric(1)]
Lower bounds on the parameters.

If a single number, this will be applied to all parameters.

Can be NA to not define any bounds.

upper

[NA | numeric() | numeric(1)]
Upper bounds on the parameters.

If a single number, this will be applied to all parameters.

Can be NA to not define any bounds.

...

[any]
In Optimizer$new() and $set_arguments(), named arguments to pass to the optimizer algorithm. In $minimize(), $maximize(), and $optimize(), named arguments to pass to the objective function.

Returns

A named list, containing at least these five elements:

value

A numeric, the maximum function value.

parameter

A numeric vector, the parameter vector where the maximum is obtained.

seconds

A numeric, the optimization time in seconds.

initial

A numeric, the initial parameter values.

error

A logical flag: TRUE if an error occurred, otherwise FALSE.

Additional output elements from the optimizer are appended.

If an error occurred, the error message is also appended as element error_message.

If the time limit was exceeded, this also counts as an error. In addition, the flag time_out = TRUE is appended.

Examples

Optimizer$new("stats::nlm")$
  maximize(objective = function(x) -x^4 + 3*x - 5, initial = 2)

Method optimize()

Perform minimization or maximization.

Usage

Optimizer$optimize(
  objective,
  initial,
  lower = NA,
  upper = NA,
  direction = "min",
  ...
)

Arguments

objective

[function | Objective]
A function to be optimized that

  1. has at least one argument that receives a numeric vector

  2. and returns a single numeric value.

Alternatively, it can also be an Objective object for more flexibility.

initial

[numeric()]
Starting parameter values for the optimization.

lower

[NA | numeric() | numeric(1)]
Lower bounds on the parameters.

If a single number, this will be applied to all parameters.

Can be NA to not define any bounds.

upper

[NA | numeric() | numeric(1)]
Upper bounds on the parameters.

If a single number, this will be applied to all parameters.

Can be NA to not define any bounds.

direction

[character(1)]
Either "min" for minimization or "max" for maximization.

...

[any]
In Optimizer$new() and $set_arguments(), named arguments to pass to the optimizer algorithm. In $minimize(), $maximize(), and $optimize(), named arguments to pass to the objective function.

Returns

A named list, containing at least these five elements:

value

A numeric, the optimized function value.

parameter

A numeric vector, the parameter vector where the optimum is obtained.

seconds

A numeric, the optimization time in seconds.

initial

A numeric, the initial parameter values.

error

A logical flag: TRUE if an error occurred, otherwise FALSE.

Additional output elements from the optimizer are appended.

If an error occurred, the error message is also appended as element error_message.

If the time limit was exceeded, this also counts as an error. In addition, the flag time_out = TRUE is appended.

Examples

objective <- function(x) -x^4 + 3*x - 5
optimizer <- Optimizer$new("stats::nlm")
optimizer$optimize(
  objective = objective, initial = 2, direction = "min"
)
optimizer$optimize(
  objective = objective, initial = 2, direction = "max"
)

Method print()

Prints the optimizer label.

Usage

Optimizer$print(...)

Arguments

...

[any]
In Optimizer$new() and $set_arguments(), named arguments to pass to the optimizer algorithm. In $minimize(), $maximize(), and $optimize(), named arguments to pass to the objective function.

Returns

Invisibly the Optimizer object.

Method clone()

The objects of this class are cloneable with this method.

Usage

Optimizer$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

### Task: compare minimization with 'stats::nlm' and 'pracma::nelder_mead'

# 1. define objective function and initial values
objective <- TestFunctions::TF_ackley
initial <- c(3, 3)

# 2. get overview of optimizers available in dictionary
optimizer_dictionary$keys
#> [1] "lbfgsb3c::lbfgsb3c" "lbfgsb3c::lbfgsb3"  "lbfgsb3c::lbfgsb3f"
#> [4] "lbfgsb3c::lbfgsb3x" "stats::nlm"         "stats::nlminb"     
#> [7] "stats::optim"       "ucminf::ucminf"    

# 3. define 'nlm' optimizer
nlm <- Optimizer$new(which = "stats::nlm")

# 4. define the 'pracma::nelder_mead' optimizer
#    (not contained in the dictionary)
nelder_mead <- Optimizer$new(which = "custom")
#> Use method `$definition()` next to define a custom optimizer.
nelder_mead$definition(
  algorithm = pracma::nelder_mead, # optimization function
  arg_objective = "fn",            # objective function argument name
  arg_initial = "x0",              # argument name for the initial values
  out_value = "fmin",              # element for the optimal function value
  out_parameter = "xmin",          # element for the optimal parameters
  direction = "min"                # optimizer minimizes
)

# 5. compare the minimization results
nlm$minimize(objective, initial)
#> $value
#> [1] 6.559645
#> 
#> $parameter
#> [1] 1.974451 1.974451
#> 
#> $seconds
#> [1] 0.03510308
#> 
#> $initial
#> [1] 3 3
#> 
#> $error
#> [1] FALSE
#> 
#> $gradient
#> [1] 5.757896e-08 5.757896e-08
#> 
#> $code
#> [1] 1
#> 
#> $iterations
#> [1] 6
#> 
nelder_mead$minimize(objective, initial)
#> $value
#> [1] 4.440892e-16
#> 
#> $parameter
#> [1] 0 0
#> 
#> $seconds
#> [1] 0.0263989
#> 
#> $initial
#> [1] 3 3
#> 
#> $error
#> [1] FALSE
#> 
#> $count
#> [1] 105
#> 
#> $info
#> $info$solver
#> [1] "Nelder-Mead"
#> 
#> $info$restarts
#> [1] 0
#> 
#> 


## ------------------------------------------------
## Method `Optimizer$minimize`
## ------------------------------------------------

Optimizer$new("stats::nlm")$
  minimize(objective = function(x) x^4 + 3*x - 5, initial = 2)
#> $value
#> [1] -7.044261
#> 
#> $parameter
#> [1] -0.9085614
#> 
#> $seconds
#> [1] 0.003821611
#> 
#> $initial
#> [1] 2
#> 
#> $error
#> [1] FALSE
#> 
#> $gradient
#> [1] -6.067147e-06
#> 
#> $code
#> [1] 1
#> 
#> $iterations
#> [1] 7
#> 

## ------------------------------------------------
## Method `Optimizer$maximize`
## ------------------------------------------------

Optimizer$new("stats::nlm")$
  maximize(objective = function(x) -x^4 + 3*x - 5, initial = 2)
#> $value
#> [1] -2.955739
#> 
#> $parameter
#> [1] 0.9085598
#> 
#> $seconds
#> [1] 0.004701614
#> 
#> $initial
#> [1] 2
#> 
#> $error
#> [1] FALSE
#> 
#> $gradient
#> [1] -3.801404e-07
#> 
#> $code
#> [1] 1
#> 
#> $iterations
#> [1] 8
#> 

## ------------------------------------------------
## Method `Optimizer$optimize`
## ------------------------------------------------

objective <- function(x) -x^4 + 3*x - 5
optimizer <- Optimizer$new("stats::nlm")
optimizer$optimize(
  objective = objective, initial = 2, direction = "min"
)
#> $value
#> [1] -1.012458e+16
#> 
#> $parameter
#> [1] 10031
#> 
#> $seconds
#> [1] 0.003214598
#> 
#> $initial
#> [1] 2
#> 
#> $error
#> [1] FALSE
#> 
#> $gradient
#> [1] -4.037322e+12
#> 
#> $code
#> [1] 5
#> 
#> $iterations
#> [1] 6
#> 
optimizer$optimize(
  objective = objective, initial = 2, direction = "max"
)
#> $value
#> [1] -2.955739
#> 
#> $parameter
#> [1] 0.9085598
#> 
#> $seconds
#> [1] 0.004770517
#> 
#> $initial
#> [1] 2
#> 
#> $error
#> [1] FALSE
#> 
#> $gradient
#> [1] -3.801404e-07
#> 
#> $code
#> [1] 1
#> 
#> $iterations
#> [1] 8
#>