Zobrazeno 1 - 10
of 80
pro vyhledávání: '"J B, Rosen"'
Nonlinear Programming contains the proceedings of a Symposium on Nonlinear Programming held in Madison, Wisconsin on May 4-6, 1970. This book emphasizes algorithms and related theories that lead to efficient computational methods for solving nonlinea
Autor:
John Glick, J. B. Rosen
Publikováno v:
Journal of Global Optimization. 36:461-469
The problem of approximating m data points (x i , y i ) in $$\mathfrak{R}^{n+1}$$ , with a quadratic function q(x, p) with s parameters, m ? s, is considered. The parameter vector $$p\in \mathfrak{R}^s$$ is to be determined so as to satisfy three con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e67287fa3c8710794a8ab2562684068
https://doi.org/10.1007/s10898-006-9021-4
https://doi.org/10.1007/s10898-006-9021-4
Publikováno v:
Journal of Global Optimization. 34:475-488
Motivated by the fact that important real-life problems, such as the protein docking problem, can be accurately modeled by minimizing a nonconvex piecewise-quadratic function, a nonconvex underestimator is constructed as the minimum of a finite numbe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c71a7a8d1f52dbab0074ec76d9339210
https://doi.org/10.1007/s10898-005-3845-1
https://doi.org/10.1007/s10898-005-3845-1
Publikováno v:
Computational Optimization and Applications. 34:35-45
A function on Rn with multiple local minima is approximated from below, via linear programming, by a linear combination of convex kernel functions using sample points from the given function. The resulting convex kernel underestimator is then minimiz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0719e457d9e0cb65ee4983bac941041
https://doi.org/10.1007/s10589-005-3072-1
https://doi.org/10.1007/s10589-005-3072-1
Publikováno v:
Journal of Global Optimization. 32:1-9
Given a function on Rn with many multiple local minima we approximate it from below, via concave minimization, with a piecewise-linear convex function by using sample points from the given function. The piecewise-linear function is then minimized usi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b4a6b1b4258af472bdfa7b112c57b3d
https://doi.org/10.1007/s10898-004-5907-1
https://doi.org/10.1007/s10898-004-5907-1
Publikováno v:
Biophysical Journal. 86:411-419
Identifying the fold class of a protein sequence of unknown structure is a fundamental problem in modern biology. We apply a supervised learning algorithm to the classification of protein sequences with low sequence identity from a library of 174 str
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dacd18a38c0d1969c524164429af64e2
https://doi.org/10.1016/s0006-3495(04)74117-x
https://doi.org/10.1016/s0006-3495(04)74117-x
Publikováno v:
Journal of Computational Chemistry. 24:89-97
We present a method called MOPED for optimizing energetic and structural parameters in computational models, including all-atom energy functions, when native structures and decoys are given. The present method goes beyond previous approaches in treat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b81f9bc01a3e0fe7d0ae50efd787516a
https://doi.org/10.1002/jcc.10124
https://doi.org/10.1002/jcc.10124
Publikováno v:
Biophysical Journal. 79:2818-2824
Models in computational biology, such as those used in binding, docking, and folding, are often empirical and have adjustable parameters. Because few of these models are yet fully predictive, the problem may be nonoptimal choices of parameters. We de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70095f225b8db8e9d510513ebbe39bae
https://doi.org/10.1016/s0006-3495(00)76520-9
https://doi.org/10.1016/s0006-3495(00)76520-9
Publikováno v:
Journal of Computational Chemistry. 20:1527-1532
We provide some tests of the convex global underestimator (CGU) algorithm, which aims to find global minima on funnel-shaped energy landscapes. We use two different potential functions—the reduced Lennard–Jones cluster potential, and the modified
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ec420040ada7e5b33ce6c126a2d9354
https://doi.org/10.1002/(sici)1096-987x(19991115)20:14<1527::aid-jcc5>3.0.co
https://doi.org/10.1002/(sici)1096-987x(19991115)20:14<1527::aid-jcc5>3.0.co