feat: optionally allow numerical gradient approximations for OptimizerBatchNLoptr

Author: sumny

R/OptimizerBatchNLoptr.R

@@ -4,8 +4,8 @@
 #' @name mlr_optimizers_nloptr
 #'
 #' @description
-#' `OptimizerBatchNLoptr` class that implements non-linear optimization. Calls
-#' [nloptr::nloptr()] from package \CRANpkg{nloptr}.
+#' `OptimizerBatchNLoptr` class that implements non-linear optimization.
+#' Calls [nloptr::nloptr()] from package \CRANpkg{nloptr}.
 #'
 #' @section Parameters:
 #' \describe{
@@ -16,8 +16,13 @@
 #' \item{`ftol_rel`}{`numeric(1)`}
 #' \item{`ftol_abs`}{`numeric(1)`}
 #' \item{`start_values`}{`character(1)`\cr
-#' Create `random` start values or based on `center` of search space? In the
-#' latter case, it is the center of the parameters before a trafo is applied.}
+#' Create `random` start values or based on `center` of search space?
+#' In the latter case, it is the center of the parameters before a trafo is applied.}
+#' \item{`approximate_eval_grad_f`}{`logical(1)`\cr
+#' Should gradients be numerically approximated via finite differences ([nloptr::nl.grad]).
+#' Only required for certain algorithms.
+#' Note that function evaluations required for the numerical gradient approximation will be logged as usual
+#' and are not treated differently than regular function evaluations by, e.g., [Terminator]s.}
 #' }
 #'
 #' For the meaning of the control parameters, see [nloptr::nloptr()] and
@@ -82,24 +87,27 @@ OptimizerBatchNLoptr = R6Class("OptimizerBatchNLoptr", inherit = OptimizerBatch,
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(
-        algorithm = p_fct(levels = c(
-          "NLOPT_GN_DIRECT_L", "NLOPT_GN_DIRECT_L_RAND", "NLOPT_GN_DIRECT_NOSCAL", "NLOPT_GN_DIRECT_L_NOSCAL",
-          "NLOPT_GN_DIRECT_L_RAND_NOSCAL", "NLOPT_GN_ORIG_DIRECT", "NLOPT_GN_ORIG_DIRECT_L", "NLOPT_GD_STOGO",
-          "NLOPT_GD_STOGO_RAND", "NLOPT_LD_SLSQP", "NLOPT_LD_LBFGS_NOCEDAL", "NLOPT_LD_LBFGS", "NLOPT_LN_PRAXIS",
-          "NLOPT_LD_VAR1", "NLOPT_LD_VAR2", "NLOPT_LD_TNEWTON", "NLOPT_LD_TNEWTON_RESTART", "NLOPT_LD_TNEWTON_PRECOND",
-          "NLOPT_LD_TNEWTON_PRECOND_RESTART", "NLOPT_GN_CRS2_LM", "NLOPT_GN_MLSL", "NLOPT_GD_MLSL", "NLOPT_GN_MLSL_LDS",
-          "NLOPT_GD_MLSL_LDS", "NLOPT_LD_MMA", "NLOPT_LD_CCSAQ", "NLOPT_LN_COBYLA", "NLOPT_LN_NEWUOA",
-          "NLOPT_LN_NEWUOA_BOUND", "NLOPT_LN_NELDERMEAD", "NLOPT_LN_SBPLX", "NLOPT_LN_AUGLAG", "NLOPT_LD_AUGLAG",
-          "NLOPT_LN_AUGLAG_EQ", "NLOPT_LD_AUGLAG_EQ", "NLOPT_LN_BOBYQA", "NLOPT_GN_ISRES"),
-        tags = "required"),
+        algorithm = p_fct(
+          levels = c(
+            "NLOPT_GN_DIRECT_L", "NLOPT_GN_DIRECT_L_RAND", "NLOPT_GN_DIRECT_NOSCAL", "NLOPT_GN_DIRECT_L_NOSCAL",
+            "NLOPT_GN_DIRECT_L_RAND_NOSCAL", "NLOPT_GN_ORIG_DIRECT", "NLOPT_GN_ORIG_DIRECT_L", "NLOPT_GD_STOGO",
+            "NLOPT_GD_STOGO_RAND", "NLOPT_LD_SLSQP", "NLOPT_LD_LBFGS_NOCEDAL", "NLOPT_LD_LBFGS", "NLOPT_LN_PRAXIS",
+            "NLOPT_LD_VAR1", "NLOPT_LD_VAR2", "NLOPT_LD_TNEWTON", "NLOPT_LD_TNEWTON_RESTART", "NLOPT_LD_TNEWTON_PRECOND",
+            "NLOPT_LD_TNEWTON_PRECOND_RESTART", "NLOPT_GN_CRS2_LM", "NLOPT_GN_MLSL", "NLOPT_GD_MLSL", "NLOPT_GN_MLSL_LDS",
+            "NLOPT_GD_MLSL_LDS", "NLOPT_LD_MMA", "NLOPT_LD_CCSAQ", "NLOPT_LN_COBYLA", "NLOPT_LN_NEWUOA",
+            "NLOPT_LN_NEWUOA_BOUND", "NLOPT_LN_NELDERMEAD", "NLOPT_LN_SBPLX", "NLOPT_LN_AUGLAG", "NLOPT_LD_AUGLAG",
+            "NLOPT_LN_AUGLAG_EQ", "NLOPT_LD_AUGLAG_EQ", "NLOPT_LN_BOBYQA", "NLOPT_GN_ISRES"),
+          tags = "required"),
         eval_g_ineq = p_uty(default = NULL),
         xtol_rel = p_dbl(default = 10^-4, lower = 0, upper = Inf, special_vals = list(-1)),
         xtol_abs = p_dbl(default = 0, lower = 0, upper = Inf, special_vals = list(-1)),
         ftol_rel = p_dbl(default = 0, lower = 0, upper = Inf, special_vals = list(-1)),
         ftol_abs = p_dbl(default = 0, lower = 0, upper = Inf, special_vals = list(-1)),
-        start_values = p_fct(default = "random", levels = c("random", "center"))
+        start_values = p_fct(default = "random", levels = c("random", "center")),
+        approximate_eval_grad_f = p_lgl(default = FALSE)
       )
       param_set$values$start_values = "random"
+      param_set$values$approximate_eval_grad_f = FALSE
 
       super$initialize(
         id = "nloptr",
@@ -116,18 +124,34 @@ OptimizerBatchNLoptr = R6Class("OptimizerBatchNLoptr", inherit = OptimizerBatch,
   private = list(
     .optimize = function(inst) {
       pv = self$param_set$values
+
       pv$x0 = search_start(inst$search_space, type = pv$start_values)
       pv$start_values = NULL
+
+      eval_grad_f = if (pv$approximate_eval_grad_f) {
+        function(x) {
+          invoke(nloptr::nl.grad, x0 = x, fn = inst$objective_function)
+        }
+      } else {
+        NULL
+      }
+      pv$eval_grad_f = eval_grad_f
+      pv$approximate_eval_grad_f = NULL
+
       opts = pv[which(names(pv) %nin% formalArgs(nloptr::nloptr))]
-      # Deactivate termination criterions which are replaced by Terminators
+      # deactivate termination criterions which are replaced by Terminators
       opts = insert_named(opts, list(
         maxeval = -1,
         maxtime = -1,
         stopval = -Inf
       ))
       pv = pv[which(names(pv) %nin% names(opts))]
-      invoke(nloptr::nloptr, eval_f = inst$objective_function,
-        lb = inst$search_space$lower, ub = inst$search_space$upper, opts = opts,
+
+      invoke(nloptr::nloptr,
+        eval_f = inst$objective_function,
+        lb = inst$search_space$lower,
+        ub = inst$search_space$upper,
+        opts = opts,
         .args = pv)
     }
   )

man/ArchiveAsync.Rd

@@ -5,7 +5,21 @@
     Output
       <OptimizerBatchNLoptr>: Non-linear Optimization
       * Parameters: algorithm=NLOPT_LN_BOBYQA, xtol_rel=-1, xtol_abs=-1,
-        ftol_rel=-1, ftol_abs=-1, start_values=random
+        ftol_rel=-1, ftol_abs=-1, start_values=random,
+        approximate_eval_grad_f=FALSE
+      * Parameter classes: ParamDbl
+      * Properties: single-crit
+      * Packages: bbotk, nloptr
+
+---
+
+    Code
+      z$optimizer
+    Output
+      <OptimizerBatchNLoptr>: Non-linear Optimization
+      * Parameters: algorithm=NLOPT_LD_LBFGS, xtol_rel=-1, xtol_abs=-1,
+        ftol_rel=-1, ftol_abs=-1, start_values=random,
+        approximate_eval_grad_f=TRUE
       * Parameter classes: ParamDbl
       * Properties: single-crit
       * Packages: bbotk, nloptr

man/ArchiveAsyncFrozen.Rd

@@ -59,3 +59,4 @@ test_that("OptimizerBatchChain", {
       "OptimizerBatchGenSA_1.verbose", "OptimizerBatchGenSA_1.trace.mat")
   )
 })
+

man/OptimInstanceAsync.Rd

@@ -7,4 +7,9 @@ test_that("OptimizerBatchNLoptr", {
     term_evals = 5L)
   expect_class(z$optimizer, "OptimizerBatchNLoptr")
   expect_snapshot(z$optimizer)
+
+  z = test_optimizer_1d("nloptr", algorithm = "NLOPT_LD_LBFGS", approximate_eval_grad_f = TRUE,
+    xtol_rel = -1, xtol_abs = -1, ftol_rel = -1, ftol_abs = -1, term_evals = 5L)
+  expect_class(z$optimizer, "OptimizerBatchNLoptr")
+  expect_snapshot(z$optimizer)
 })