# High-dimensional Black-box Optimization via Divide and Approximate Conquer

arXiv: https://arxiv.org/abs/1603.03518 · HTML: https://mlsanity.com/paper/1603.03518/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2016.

## Built on by (most cited first, 4 of 4)

- [Derivative-Free Reinforcement Learning: A Review](https://mlsanity.com/paper/2102.05710/) (2021)
- [Evolutionary Reinforcement Learning via Cooperative Coevolution](https://mlsanity.com/paper/2404.14763/) (2024)
- [Variable Division and Optimization for Constrained Multiobjective Portfolio Problems](https://mlsanity.com/paper/2101.08552/) (2021)
- [A Dynamic Aggregation Strategy Enhanced Efficient Global Optimization Algorithm for Solving High-Dimensional Turbomachinery Design Problems](https://mlsanity.com/paper/2609.16067/) (2026)

## Abstract (arXiv)

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.