  Bayesian optimization is known to be difficult to scale to high dimensions,
because the acquisition step requires solving a non-convex optimization problem
in the same search space. In order to scale the method and keep its benefits,
we propose an algorithm (LineBO) that restricts the problem to a sequence of
iteratively chosen one-dimensional sub-problems that can be solved efficiently.
We show that our algorithm converges globally and obtains a fast local rate
when the function is strongly convex. Further, if the objective has an
invariant subspace, our method automatically adapts to the effective dimension
without changing the algorithm. When combined with the SafeOpt algorithm to
solve the sub-problems, we obtain the first safe Bayesian optimization
algorithm with theoretical guarantees applicable in high-dimensional settings.
We evaluate our method on multiple synthetic benchmarks, where we obtain
competitive performance. Further, we deploy our algorithm to optimize the beam
intensity of the Swiss Free Electron Laser with up to 40 parameters while
satisfying safe operation constraints.
