docs: update NEWS

Author: sumny

NEWS.md

@@ -1,9 +1,9 @@
 # mlr3mbo (development version)
 
-* refactor: refactored `SurrogateLearner` and `SurrogateLearnerCollection` to allow updating on an asynchronous `Archive`
-* feat: added experimental `OptimizerAsyncMbo`, `OptimizerADBO`, `TunerAsyncMbo`, and `TunerADBO` that allow for asynchronous optimization
-* feat: added `AcqFunctionStochasticCB` and `AcqFunctionStochasticEI` that are useful for asynchronous optimization
-* doc: minor changes to highlight differences between batch and asynchronous objects related to asynchronous support
+* refactor: refactored `SurrogateLearner` and `SurrogateLearnerCollection` to allow updating on an asynchronous `Archive`.
+* feat: added experimental `OptimizerAsyncMbo`, `OptimizerADBO`, `TunerAsyncMbo`, and `TunerADBO` that allow for asynchronous optimization.
+* feat: added `AcqFunctionStochasticCB` and `AcqFunctionStochasticEI` that are useful for asynchronous optimization.
+* doc: minor changes to highlight differences between batch and asynchronous objects related to asynchronous support.
 * refactor: `AcqFunction`s and `AcqOptimizer` gained a `reset()` method.
 
 # mlr3mbo 0.2.6

README.Rmd

@@ -81,7 +81,7 @@ acq_function = acqf("ei")
 
 acq_optimizer = acqo(
   opt("local_search", n_initial_points = 10, initial_random_sample_size = 1000, neighbors_per_point = 10),
-  terminator = trm("evals", n_evals = 3000)
+  terminator = trm("evals", n_evals = 2000)
 )
 
 optimizer = opt("mbo",

README.md

@@ -87,7 +87,7 @@ acq_function = acqf("ei")
 
 acq_optimizer = acqo(
   opt("local_search", n_initial_points = 10, initial_random_sample_size = 1000, neighbors_per_point = 10),
-  terminator = trm("evals", n_evals = 3000)
+  terminator = trm("evals", n_evals = 2000)
 )
 
 optimizer = opt("mbo",
@@ -100,9 +100,9 @@ optimizer = opt("mbo",
 optimizer$optimize(instance)
 ```
 
-    ##          x1       x2  x_domain         y
-    ##       <num>    <num>    <list>     <num>
-    ## 1: 3.090821 2.299709 <list[2]> 0.4104925
+    ##          x1       x2  x_domain        y
+    ##       <num>    <num>    <list>    <num>
+    ## 1: 3.104516 2.396279 <list[2]> 0.412985
 
 We can quickly visualize the contours of the objective function (on log
 scale) as well as the sampling behavior of our BO run (lighter blue