Prepares the optimizer for use with a specific function and starting point.
Usage
mize_init(
opt,
par,
fg,
max_iter = Inf,
max_fn = Inf,
max_gr = Inf,
max_fg = Inf,
abs_tol = NULL,
rel_tol = abs_tol,
grad_tol = NULL,
ginf_tol = NULL,
step_tol = NULL
)Arguments
- opt
Optimizer, created by
make_mize().- par
Vector of initial values for the function to be optimized over.
- fg
Function and gradient list. See the documentation of
mize().- max_iter
(Optional). Maximum number of iterations. See the 'Convergence' section of
mize()for details.- max_fn
(Optional). Maximum number of function evaluations. See the 'Convergence' section of
mize()for details.- max_gr
(Optional). Maximum number of gradient evaluations. See the 'Convergence' section of
mize()for details.- max_fg
(Optional). Maximum number of function or gradient evaluations. See the 'Convergence' section of
mize()for details.- abs_tol
(Optional). Absolute tolerance for comparing two function evaluations. See the 'Convergence' section of
mize()for details.- rel_tol
(Optional). Relative tolerance for comparing two function evaluations. See the 'Convergence' section of
mize()for details.- grad_tol
(Optional). Absolute tolerance for the length (l2-norm) of the gradient vector. See the 'Convergence' section of
mize()for details.- ginf_tol
(Optional). Absolute tolerance for the infinity norm (maximum absolute component) of the gradient vector. See the 'Convergence' section of
mize()for details.- step_tol
(Optional). Absolute tolerance for the size of the parameter update. See the 'Convergence' section of
mize()for details.
Details
Should be called after creating an optimizer with make_mize() and
before beginning any optimization with mize_step(). Alternatively,
if fg and par are available when calling
make_mize(), they can be passed to that function and the returned
optimizer will already be initialized. mize_step() requires an
initialized optimizer and does not carry out initialization itself.
Reinitialization resets the iteration counter and transient algorithm state,
but preserves accumulated function, gradient, Hessian, and inverse-Hessian
evaluation counts and explicitly registered custom hooks. Initialization
itself may extend the curvature counts: PHESS requests fg$hs, while BFGS
and SR1 request a supplied fg$hi as part of initialization.
Initialization does not guarantee that the objective or gradient has been
evaluated at par. If caller-owned records need a starting observation,
request it with mize_step_summary() using calc_fn = TRUE or
calc_gr = TRUE, and retain the opt returned by that call. Any requested
callback still honors the configured hard budgets.
Optional convergence parameters may also be passed here, for use with
check_mize_convergence(). They are optional if you do your own
convergence checking.
Details of the fg list can be found in the 'Details' section of
mize().
Examples
# Create an optimizer
opt <- make_mize(method = "L-BFGS")
# Function to optimize and starting point defined after creating optimizer
rosenbrock_fg <- list(
fn = function(x) {
100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2
},
gr = function(x) {
c(
-400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]),
200 * (x[2] - x[1] * x[1])
)
}
)
rb0 <- c(-1.2, 1)
# Initialize with function and starting point before commencing optimization
opt <- mize_init(opt, rb0, rosenbrock_fg, max_iter = 3)
# Finally, commence a terminal-aware optimization loop
par <- rb0
while (!opt$is_terminated) {
par_old <- par
step_result <- mize_step(opt, par, rosenbrock_fg)
opt <- step_result$opt
par <- step_result$par
if (opt$is_terminated) {
break
}
step_info <- mize_step_summary(opt, par, rosenbrock_fg, par_old)
opt <- step_info$opt
if (opt$is_terminated) {
break
}
opt <- check_mize_convergence(step_info)
}