A Derivative-Free Optimization Algorithm Using Sparse Grid Integration


We present a new derivative-free optimization algorithm based on the sparse grid numerical integration. The algorithm applies to a smooth nonlinear objective function where calculating its gradient is impossible and evaluating its value is also very expensive. The new algorithm has: 1) a unique starting point strategy; 2) an effective global search heuristic; and 3) consistent local convergence. These are achieved through a uniform use of sparse grid numerical integration. Numerical experiment result indicates that the algorithm is accurate and efficient, and benchmarks favourably against several state-of-art derivative free algorithms.

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S. Chen and X. Wang, "A Derivative-Free Optimization Algorithm Using Sparse Grid Integration," American Journal of Computational Mathematics, Vol. 3 No. 1, 2013, pp. 16-26. doi: 10.4236/ajcm.2013.31003.

Conflicts of Interest

The authors declare no conflicts of interest.


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