sim_anneal

This function implements the simulated annealing algorithm, which is a global optimization algorithm that is useful for finding a good starting point for a local optimization algorithm. We do not return this as an MLE object because, to be a good estimate of the MLE, the gradient of `f` evaluated at its solution should be close to zero, assuming the MLE is interior to the domain of `f`. However, since this algorithm is not guided by gradient information, it is not sensitive to the gradient of `f` and instead only seeks to maximize `f`.

Usage

sim_anneal(x0 = NULL, obj_fn = NULL, options = list(), ...)

Arguments

x0

Initial guess, default is NULL (must be specified in options)

obj_fn

Objective function to maximize, default is NULL (must be specified in options)

options

List of optional arguments

...

Additional arguments that may be passed to `options$neigh`

Value

list with best solution (argmax) and its corresponding objective function value (max), and optionally path

Functions

• sim_anneal(): options

Fields

t_init

Initial temperature

t_end

Final temperature

alpha

Cooling factor

iter_per_temp

Number of iterations per temperature

max_iter

Maximum number of iterations, used instead of t_end if not NULL, defaults to NULL

debug

If TRUE, print debugging information to the console

trace

If TRUE, track the history of positions and values

sup

Support function, returns TRUE if x is in the domain of f

neigh

Neighborhood function, returns a random neighbor of x

debug_freq

Frequency of debug output, defaults to 10

obj_fn

Objective function to maximize, if not specified in formal parameter `obj_fn`

x0

Initial guess, if not specified in formal parameter `x0`