Abstract
Using a new formulation of both exact and general molecular distance geometry problems we obtain a smooth d.c. (difference of convex functions) program for them. A d.c. optimization algorithm (DCA) has been proposed. We investigate differents techniques that improve the computational efficiency of the algorithm. An important issue in the d.c. optimization approach, the nice effect of d.c. decompositions of the objective function is well exploited. Numerical experiments on the data derived from PDB data bank up to 4189 atoms (32400 variables) are presented which show the efficiency of the proposed algorithm.
This work was partially supported by the computer resources financed by “Contrat de Plan Interrégional du Bassin Parisien - Pôle interrégional de modélisation en Sciences pour Ingénieurs”
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An, L.T.H., Tao, P.D. (2003). A new algorithm for Solving Large Scale Molecular Distance Geometry Problems. In: Di Pillo, G., Murli, A. (eds) High Performance Algorithms and Software for Nonlinear Optimization. Applied Optimization, vol 82. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0241-4_13
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DOI: https://doi.org/10.1007/978-1-4613-0241-4_13
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