Files changed (4)
-\b demo_reembo evaluates the REEMBO algorithm for optimization in very high dimensions. The idea is that Bayesian optimization can be used very high dimensions provided that the effective dimension is embedded in a lower space, by using random projections.
-In this case, we test it against an artificially augmented Branin function with 1000 dimensions where only 2 dimensions are actually relevant (but unknown). The function is defined in the file: \c braninghighdim