Set optimization settings

Source: R/task_optimization.R

setOptimizationSettings sets optimization task settings including parameters, experiments and method options.

setOptimizationSettings(
  expression = NULL,
  maximize = NULL,
  subtask = NULL,
  randomize_start_values = NULL,
  calculate_statistics = NULL,
  update_model = NULL,
  executable = NULL,
  parameters = NULL,
  method = NULL,
  model = getCurrentModel()
)

setOpt(
  expression = NULL,
  maximize = NULL,
  subtask = NULL,
  randomize_start_values = NULL,
  calculate_statistics = NULL,
  update_model = NULL,
  executable = NULL,
  parameters = NULL,
  method = NULL,
  model = getCurrentModel()
)

Arguments

expression

Expression to optimize, as string.

maximize

flag

subtask

string

Available tasks: 'steadyState', 'timeCourse', 'scan', 'fluxMode', 'optimization', 'parameterFitting', 'mca', 'lyap', 'tssAnalysis', 'sens', 'moieties', 'crosssection', 'lna', 'analytics', 'timeSens'.

randomize_start_values

flag

calculate_statistics

flag

update_model

flag

executable

flag

parameters

corc_opt_parm or list of corc_opt_parm objects

See also defineOptimizationParameter.

method

string or list

Set the method directly with a string or with a list containing the entry named `method`.

The list may contain further method arguments and their values.

Available methods: 'Statistics', 'DifferentialEvolution', 'SRES', 'EvolutionaryProgram', 'GeneticAlgorithm', 'GeneticAlgorithmSR', 'HookeJeeves', 'LevenbergMarquardt', 'NelderMead', 'ParticleSwarm', 'Praxis', 'RandomSearch', 'ScatterSearch', 'SimulatedAnnealing', 'SteepestDescent', 'TruncatedNewton'.

model

a model object

Details

The online article on managing tasks provides some further context.