High-dimensional Black-box Optimization via Divide and Approximate
  Conquer
  Divide and Conquer (DC) is conceptually well suited to high-dimensional
optimization by decomposing a problem into multiple small-scale sub-problems.
However, appealing performance can be seldom observed when the sub-problems are
interdependent. This paper suggests that the major difficulty of tackling
interdependent sub-problems lies in the precise evaluation of a partial
solution (to a sub-problem), which can be overwhelmingly costly and thus makes
sub-problems non-trivial to conquer. Thus, we propose an approximation
approach, named Divide and Approximate Conquer (DAC), which reduces the cost of
partial solution evaluation from exponential time to polynomial time.
Meanwhile, the convergence to the global optimum (of the original problem) is
still guaranteed. The effectiveness of DAC is demonstrated empirically on two
sets of non-separable high-dimensional problems.
