optim_refit: Refined optimization test.
In mlysy/optimCheck: Graphical and Numerical Checks for Mode-Finding Routines

Description Usage Arguments Details Value See Also

If the potential solution is indeed a local optimum of the objective function, and if it is used to initialize a second optimization, then original and "refined" solutions ought to be close.

1 2	optim_refit(xsol, fun, maximize = TRUE, maxit = 5000, reltol = 1e-08, xopt)

`xsol`	Potential solution vector of length `nx`.
`fun`	Objective function to be maximized (or minimized), with first argument the length-`nx` parameter vector over which optimization is to take place. Should return a scalar result.
`maximize`	Logical, whether a maximum or a minimum of the objective function is sought.
`maxit`	Maximum number of iterations for `optim` refit (see Details).
`reltol`	Relative tolerance for convergence of `optim` refit (see Details).
`xopt`	Optional refit solution calculated externally from an optimization algorithm of choice (see Details).

By default, a so-called **refi**ned op(**t**)imization (or refit) test is performed by running the default Nelder-Mead simplex method provided by optim, initialized by the potential solution xsol. Only a simplified interface to optim's control parameters are provided here.

Alternatively, the refit test can be performed with any optimization algorithm of choice. This is done externally, with the refined solution passed to optim_refit via the argument xopt.

An object of class optrefit inheriting from optcheck, with elements:

xsol: The potential solution.
ysol: The value of fun(xsol).
maximize: Logical indicating whether the potential solution should maximize or minimize the objective function.
xopt: The solution found by the general-purpose optimizer.
yopt: The function value at the optimal solution, i.e., fun(xopt).