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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.
functional form of composite likelihoods is derived by minimizing the
Kullback-Leibler distance under structural constraints associated with low
dimensional densities. Connections with the I-projection
and the maximum entropy distributions are shown. Asymptotic properties
of composite likelihood inference under the proposed information-theoretical
framework are established.