Optimizers

These functions return optimizers that meet requirements for the optim_fun input argument of fit_c3_aci, fit_c3_variable_j, fit_c4_aci, and fit_c4_aci_hyperbola. Essentially, they are wrappers for optimizers from other libraries that serve to standardize their inputs and outputs.

Usage

optimizer_deoptim(itermax, VTR = -Inf)

  optimizer_hjkb(tol, maxfeval = Inf, target = Inf)

  optimizer_nlminb(rel.tol, eval.max = 200, iter.max = 200, abs.tol = 0)

  optimizer_nmkb(tol, maxfeval = 2000, restarts.max = 10)

  optimizer_null()

Arguments

tol

A convergence tolerance value; to be passed to nmkb or hjkb via their control input arguments. A typical value is 1e-7.

maxfeval

A maximum value for the number of function evaluations to allow during optimization; to be passed to nmkb or hjkb via their control input arguments.

target

A real number restricting the absolute function value; to be passed to hjkb via its control input argument.

rel.tol

A relative convergence tolerance value; to be passed to nlminb via its control input argument. A typical value is 1e-10.

eval.max

A maximum value for the number of function evaluations; to be passed to nlminb via its control input argument.

iter.max

A maximum value for the number of iterations; to be passed to nlminb via its control input argument.

abs.tol

An absolute convergence tolerance value; to be passed to nlminb via its control input argument.

restarts.max

A maximum value for the number of restarts allowed during optimization; to be passed to nmkb via its control input argument.

itermax

The maximum number of generations to be used; to be passed to DEoptim via its control input argument. Note that when VTR is -Inf, the optimizer will always use the maximum number of generations. A typical value is 200.

VTR

The value to be reached; to be passed to DEoptim via its control input argument.

Details

optimizer_deoptim is a wrapper for DEoptim.

optimizer_hjkb is a wrapper for hjkb.

optimizer_nlminb is a wrapper for nlminb.

optimizer_nmkb is a wrapper for nmkb.

optimizer_null simply returns the initial guess without doing any optimization; it can be useful for viewing initial guesses.

See the documentation for those functions for more information about how the optimizers work.