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optimizeR: Unified Framework for Numerical Optimizers

Source: R/optimizeR-package.R
optimizeR-package.Rd

Provides a unified object-oriented framework for numerical optimizers in R. Supports minimization and maximization with any optimizer, optimization over more than one function argument, computation time measurement, and time limits for long optimization tasks.

See also

Useful links:

Author

Maintainer: Lennart Oelschläger oelschlaeger.lennart@gmail.com (ORCID)

Other contributors:

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] 0.006403208
#> 
#> $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] 0.04405069
#> 
#> $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] 0.003985643
#> 
#> $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] 0.004677057
#> 
#> $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] 0.003237486
#> 
#> $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] 0.004648447
#> 
#> $initial
#> [1] 2
#> 
#> $error
#> [1] FALSE
#> 
#> $gradient
#> [1] -3.801404e-07
#> 
#> $code
#> [1] 1
#> 
#> $iterations
#> [1] 8
#>