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These observations, and the importance of the protein folding and peptide docking problems, have propelled the introduction of new global search strategies specifically designed for these problems. In the sequel, we first outline the basics of the deterministic global optimization approach, aBB, which has been used extensively to study the protein structure prediction. This is followed by a comprehensive study of ab-initio modeling for structure prediction of single chain polypeptides. An extensive comparison of energy modeling, including solvation, entropic effects and free energy calculations, is provided for the oligopeptides.
00-01-00454) 11 N. M. ), Advances in Convex Analysis and Global Optimization, 11-30. © 2001 Kluwer Academic Publishers. , to identify as many points as possible or to minimize the number of unidentified points). In the paper the identification problems are treated as optimization problems. More sophisticated models are described by nonsmooth optimization problems. An algorithm is suggested allowing to construct a sequence of linear criterion functions which can be used for the identification of the points of the sets.
1), namely, stochastic and deterministic ap- 33 34 ADVANCES IN CONVEX ANALYSIS AND GLOBAL OPTIMIZATION proaches. Stochastic methods, such as those based on genetic algorithms  and simulated annealing , can be used to treat unconstrained nonconvex problems. However, the stochastic nature of the search strategy invalidates any claims regarding global optimality since it is impossible to obtain valid bounds on the solution of the problem. The addition of nonconvex constraints further complicates these solution schemes.