Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
Recent advances in global optimization
Recent advances in global optimization
Global Continuation for Distance Geometry Problems
SIAM Journal on Optimization
Relaxing the Assumptions of the Multilevel SingleLinkage Algorithm
Journal of Global Optimization
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
On a global optimization technique for solving a nonlinear hyperboloid least squares problem
Proceedings of the 2005 conference on Diversity in computing
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In this paper, we consider approximating global minima of zero or small residual, nonlinear least-squares problems. We propose a selective search approach based on the concept of selective minimization recently introduced in Zhang et al. (Technical Report TR99-12, Rice University, Department of Computational and Applied Mathematics MS-134, Houston, TX 77005, 1999). To test the viability of the proposed approach, we construct a simple implementation using a Levenberg-Marquardt type method combined with a multi-start scheme, and compare it with several existing global optimization techniques. Numerical experiments were performed on zero residual nonlinear least-squares problems chosen from structural biology applications and from the literature. On the problems of significant sizes, the performance of the new approach compared favorably with other tested methods, indicating that the new approach is promising for the intended class of problems.