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  • 1
    Electronic Resource
    Electronic Resource
    s.l. : American Chemical Society
    Macromolecules 28 (1995), S. 6533-6538 
    ISSN: 1520-5835
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology , Physics
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    s.l. : American Chemical Society
    Journal of the American Chemical Society 116 (1994), S. 4997-4998 
    ISSN: 1520-5126
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    [S.l.] : International Union of Crystallography (IUCr)
    Acta crystallographica 57 (2001), S. 681-685 
    ISSN: 1600-5724
    Source: Crystallography Journals Online : IUCR Backfile Archive 1948-2001
    Topics: Chemistry and Pharmacology , Geosciences , Physics
    Notes: A fast Newton method is presented for solving the entropy maximization problem in the Bayesian statistical approach to phase estimation. The method requires only O(n \log n) instead of standard O(n^3) floating point operations per iteration, while converging in the same rate as the standard Newton method. The method is described and related computational issues are discussed. Numerical results on simple test cases are also presented to demonstrate the behavior of the method.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Computational optimization and applications 2 (1993), S. 145-170 
    ISSN: 1573-2894
    Keywords: global/local minimization ; simulated annealing, renormalization group ; parallel computation ; protein folding
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The search for low energy states of molecular clusters is associated with the study of molecular conformation and especially protein folding. This paper describes a new global minimization algorithm which is effective and efficient for finding low energy states and hence stable structures of molecular clusters. The algorithm combines simulated annealing with a class of effective energy functions which are transformed from the original energy function based on the theory of renormalization groups. The algorithm converges to low energy states asymptotically, and is more efficient than a general simulated annealing method.
    Type of Medium: Electronic Resource
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  • 5
    ISSN: 1573-2916
    Keywords: Global/local minimization ; effective energy simulated annealing ; parallel computation ; protein folding
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This work studies the build-up method for the global minimization problem for molecular conformation, especially protein folding. The problem is hard to solve for large molecules using general minimization approaches because of the enormous amount of required computation. We therefore propose a build-up process to systematically “construct” the optimal molecular structures. A prototype algorithm is designed using the anisotropic effective energy simulated annealing method at each build-up stage. The algorithm has been implemented on the Intel iPSC/860 parallel computer, and tested with the Lennard-Jones microcluster conformation problem. The experiments showed that the algorithm was effective for relatively large test problems, and also very suitable for massively parallel computation. In particular, for the 72-atom Lennard-Jones microcluster, the algorithm found a structure whose energy is lower than any others found in previous studies.
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Journal of global optimization 8 (1996), S. 49-65 
    ISSN: 1573-2916
    Keywords: 49M37 ; 65Y05 ; 68Q22 ; 92-08 ; Global/local minimization ; numerical continuation ; parallel computation ; protein folding
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper presents our recent work on developing parallel algorithms and software for solving the global minimization problem for molecular conformation, especially protein folding. Global minimization problems are difficult to solve when the objective functions have many local minimizers, such as the energy functions for protein folding. In our approach, to avoid directly minimizing a “difficult” function, a special integral transformation is introduced to transform the function into a class of gradually deformed, but “smoother” or “easier” functions. An optimization procedure is then applied to the new functions successively, to trace their solutions back to the original function. The method can be applied to a large class of nonlinear partially separable functions including energy functions for molecular conformation and protein folding. Mathematical theory for the method, as a special continuation approach to global optimization, is established. Algorithms with different solution tracing strategies are developed. Different levels of parallelism are exploited for the implementation of the algorithms on massively parallel architectures.
    Type of Medium: Electronic Resource
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Journal of global optimization 15 (1999), S. 219-234 
    ISSN: 1573-2916
    Keywords: Distance geometry ; Distance constraints ; Protein structures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract We study the performance of the dgsol code for the solution of distance geometry problems with lower and upper bounds on distance constraints. The dgsol code uses only a sparse set of distance constraints, while other algorithms tend to work with a dense set of constraints either by imposing additional bounds or by deducing bounds from the given bounds. Our computational results show that protein structures can be determined by solving a distance geometry problem with dgsol and that the approach based on dgsol is significantly more reliable and efficient than multi-starts with an optimization code.
    Type of Medium: Electronic Resource
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