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Tailoring On-line Search Retrieval.
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2009
We consider the well-known line search algorithm that iteratively refines the search interval by subdivision and bracketing the optimum. In our applications, evaluations of the objective function typically require minutes or hours, so it becomes attractive to use more than the standard three steps in the subdivision, performing the evaluations in ...
Peachey, T. C., Abramson, D., Lewis, A.
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We consider the well-known line search algorithm that iteratively refines the search interval by subdivision and bracketing the optimum. In our applications, evaluations of the objective function typically require minutes or hours, so it becomes attractive to use more than the standard three steps in the subdivision, performing the evaluations in ...
Peachey, T. C., Abramson, D., Lewis, A.
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Operations Research, 1999
We present two new results for the asymmetric rendezvous problem on the line. We first show that it is never optimal for one player to be stationary during the entire search period in the two-player rendezvous. Then we consider the meeting time ofn-players in the worst case and show that it has an asymptotic behavior ofn/2 +O(logn).
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We present two new results for the asymmetric rendezvous problem on the line. We first show that it is never optimal for one player to be stationary during the entire search period in the two-player rendezvous. Then we consider the meeting time ofn-players in the worst case and show that it has an asymptotic behavior ofn/2 +O(logn).
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Balancing assembly lines with tabu search
European Journal of Operational Research, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lapierre, Sophie D. +2 more
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On the Nonmonotone Line Search
Journal of Optimization Theory and Applications, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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2003
There is no guarantee that the local algorithms in chapters 12 and 13 will converge when they are started at a point x 1 far from a solution x * to problem (P E ) or (P EI ). They can generate erratic sequences, which may by chance enter the neighborhood of a solution and then converge to it; but most often, the sequences will not converge. There exist
J. Frédéric Bonnans +3 more
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There is no guarantee that the local algorithms in chapters 12 and 13 will converge when they are started at a point x 1 far from a solution x * to problem (P E ) or (P EI ). They can generate erratic sequences, which may by chance enter the neighborhood of a solution and then converge to it; but most often, the sequences will not converge. There exist
J. Frédéric Bonnans +3 more
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Distributed On-line Bayesian Search
2005 International Conference on Collaborative Computing: Networking, Applications and Worksharing, 2006In this paper, we outline the basis for a new distributed Bayesian search scheme in which all Bayesian decision makers recognize the same performance objective but do not possess the ability to communicate with each other. Coordination among the players is achieved indirectly by tracking search performance.
A. Garcia, E. Campos, null Chenyang Li
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Searching for the silver lining
Nature Structural & Molecular Biology, 2005Work presented in this issue reveals the structure of a SNARE transmembrane domain and supports a model of exocytosis via hemifusion. Hemifusion may thus be a common intermediate in many, if not all, biological fusion reactions.
Judith M White, J David Castle
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