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Create an Optimizer

Source: R/mize_factory.R
make_mize.Rd

Factory function for creating a (possibly uninitialized) optimizer.

Usage

make_mize(
  method = "L-BFGS",
  norm_direction = FALSE,
  scale_hess = TRUE,
  memory = 5,
  cg_update = "PR+",
  preconditioner = "",
  tn_init = 0,
  tn_exit = "curvature",
  nest_q = 0,
  nest_convex_approx = FALSE,
  nest_burn_in = 0,
  step_up = 1.1,
  step_up_fun = c("*", "+"),
  step_down = NULL,
  dbd_weight = 0.1,
  line_search = "More-Thuente",
  c1 = 1e-04,
  c2 = NULL,
  step0 = NULL,
  step_next_init = NULL,
  try_newton_step = NULL,
  ls_max_fn = 20,
  ls_max_gr = Inf,
  ls_max_fg = Inf,
  ls_max_alpha_mult = Inf,
  ls_max_alpha = Inf,
  ls_safe_cubic = FALSE,
  strong_curvature = NULL,
  approx_armijo = NULL,
  mom_type = NULL,
  mom_schedule = NULL,
  mom_init = NULL,
  mom_final = NULL,
  mom_switch_iter = NULL,
  mom_linear_weight = FALSE,
  use_init_mom = FALSE,
  restart = NULL,
  restart_wait = 10,
  par = NULL,
  fg = NULL,
  max_iter = 100,
  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

method

Optimization method. See 'Details' of mize().

norm_direction

If TRUE, then the steepest descent direction is normalized to unit length. Useful for adaptive step size methods where the previous step size is used to initialize the next iteration.

scale_hess

if TRUE, the approximation to the inverse Hessian is scaled according to the method described by Nocedal and Wright (approximating an eigenvalue). Applies only to the methods BFGS (where the scaling is applied only during the first step) and L-BFGS (where the scaling is applied during every iteration). Ignored otherwise.

memory

The number of updates to store if using the L-BFGS method. Ignored otherwise. Must be a positive integer.

cg_update

Type of update to use for the "CG" method. For details see the "CG" subsection of the "Optimization Methods" section. Ignored if method is not "CG".

preconditioner

Type of preconditioner to use in Truncated Newton. Leave blank or set to "L-BFGS" to use a limited memory BFGS preconditioner. Use the "memory" parameter to control the number of updates to store. Applies only if method = "TN", or "CG", ignored otherwise.

tn_init

Type of initialization to use in inner loop of Truncated Newton. Use 0 to use the zero vector (the usual TN initialization), or "previous" to use the final result from the previous iteration, as suggested by Martens (2010). Applies only if method = "TN", ignored otherwise.

tn_exit

Type of exit criterion to use when terminating the inner CG loop of Truncated Newton method. Either "curvature" to use the standard negative curvature test, or "strong" to use the modified "strong" curvature test in TNPACK (Xie and Schlick, 1999). Applies only if method = "TN", ignored otherwise.

nest_q

Strong convexity parameter for the "NAG" method's momentum term. Must take a value between 0 (strongly convex) and 1 (results in steepest descent).Ignored unless the method is "NAG" and nest_convex_approx is FALSE.

nest_convex_approx

If TRUE, then use an approximation due to Sutskever for calculating the momentum parameter in the NAG method. Only applies if method is "NAG".

nest_burn_in

Number of iterations to wait before using a non-zero momentum. Only applies if using the "NAG" method or setting the momentum_type to "Nesterov".

step_up

Value by which to increase the step size for the "bold" step size method or the "DBD" method.

step_up_fun

Operator to use when combining the current step size with step_up. Can be one of "*" (to multiply the current step size with step_up) or "+" (to add).

step_down

Multiplier to reduce the step size by if using the "DBD" method or the "bold". Can also be used with the "back" line search method, but is optional. Should be a positive value less than 1.

dbd_weight

Weighting parameter used by the "DBD" method only, and only if no momentum scheme is provided. Must be an integer between 0 and 1.

Type of line search to use. See 'Details' of mize().

c1

Sufficient decrease parameter for Wolfe-type line searches. Should be a value between 0 and 1.

c2

Sufficient curvature parameter for line search for Wolfe-type line searches. Should be a value between c1 and 1.

step0

Initial value for the line search on the first step. See 'Details' of mize().

step_next_init

For Wolfe-type line searches only, how to initialize the line search on iterations after the first. See 'Details' of mize().

try_newton_step

For Wolfe-type line searches only, try the line step value of 1 as the initial step size whenever step_next_init suggests a step size > 1. Defaults to TRUE for quasi-Newton methods such as BFGS and L-BFGS, FALSE otherwise.

ls_max_fn

Maximum number of function evaluations allowed during a line search.

ls_max_gr

Maximum number of gradient evaluations allowed during a line search.

ls_max_fg

Maximum number of function or gradient evaluations allowed during a line search.

ls_max_alpha_mult

The maximum value that can be attained by the ratio of the initial guess for alpha for the current line search, to the final value of alpha of the previous line search. Used to stop line searches diverging due to very large initial guesses. Only applies for Wolfe-type line searches.

ls_max_alpha

Maximum value of alpha allowed during line search. Only applies for line_search = "more-thuente".

ls_safe_cubic

(Optional). If TRUE, check that cubic interpolation in the Wolfe line search does not produce too small a value. Only applies for line_search = "more-thuente".

strong_curvature

(Optional). If TRUE use the strong curvature condition in Wolfe line search. See the 'Line Search' section of mize() for details.

approx_armijo

(Optional). If TRUE use the approximate Armijo condition in Wolfe line search. See the 'Line Search' section of mize() for details.

mom_type

Momentum type, either "classical" or "nesterov".

mom_schedule

Momentum schedule. See 'Details' of mize().

mom_init

Initial momentum value.

mom_final

Final momentum value.

mom_switch_iter

For mom_schedule "switch" only, the iteration when mom_init is changed to mom_final.

mom_linear_weight

If TRUE, the gradient contribution to the update is weighted using momentum contribution.

use_init_mom

If TRUE, then the momentum coefficient on the first iteration is non-zero. Otherwise, it's zero. Only applies if using a momentum schedule.

restart

Momentum restart type. Can be one of "fn" or "gr". See 'Details' of mize().

restart_wait

Number of iterations to wait between restarts. Ignored if restart is NULL.

par

(Optional) Initial values for the function to be optimized over.

fg

(Optional). Function and gradient list. See 'Details' 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

If the function to be optimized and starting point are not present at creation time, then the optimizer should be initialized using mize_init() before being used with mize_step().

See the documentation to mize() for an explanation of all the parameters.

Details of the fg list containing the function to be optimized and its gradient can be found in the 'Details' section of mize(). It is optional for this function, but if it is passed to this function, along with the vector of initial values, par, the optimizer will be returned already initialized for this function. Otherwise, mize_init() must be called before optimization begins.

Additionally, optional convergence parameters may also be passed here, for use with check_mize_convergence(). They are optional here if you plan to call mize_init() later, or if you want to do your own convergence checking.

Examples

# Function to optimize and starting point
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)

# Create an optimizer and initialize it for use with the Rosenbrock function
opt <- make_mize(method = "L-BFGS", par = rb0, fg = rosenbrock_fg)

# Create optimizer without initialization
opt <- make_mize(method = "L-BFGS")

# Need to call mize_init separately:
opt <- mize_init(opt, rb0, rosenbrock_fg)