objective_functions: Solver objective functions
In maciejsmolka/solvergater: External Solver Gateway in R

Description Usage Arguments Value See Also Examples

Function factory producing functions for use in optimization. These are objective function and its gradient. They can be used, e.g., as parameters fn and gr of stats::optim(). Note that the functions compute their values through objective(), however, calls to the latter are memoised, so actual solver should never be called twice for the same data (x). In particular, computing value and gradient for a given x requires only a single solver run. To obtain non-memoised functions use make_functions() composed with objective() (see example in make_functions()).

1	objective_functions(solver, data, ...)

`solver`	object of class `solver`.
`data`	observed ('exact') data.
`...`	additional args passed to `objective()` or `memoise::memoise()`, note that some solvers can have some mandatory parameters (see `required_args()`), e.g. solvers created with `adaptive_shell_solver()` require `precision`.

List with one or two components:

value: objective value function,
gradient: objective gradient function, missing if solver does not compute Jacobian matrix.

memoise::memoise()

s <- fake_adaptive_solver(4, 5)
result <- run(s, c(10, 10, 10, 10), precision = 5.0, silent = TRUE)
observed_data <- result$qoi
x <- c(10.5, 9.44, 10.21, 8.14)
solver_funs <- objective_functions(s, observed_data, precision = 30.0,
silent = TRUE)
solver_funs$value(x)
solver_funs$gradient(x)