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- W121626557 abstract "We consider finite dimensional smooth optimization problems with a compact and connected feasible set. A basic problem in global optimization is how to get from one local minimum to all other ones by using local ascent or descent directions. In general, these local directions are induced by a Riemannian (i.e. variable) metric. We consider the “bang-bang” strategy of starting at a local minimum, going upward via the ascent flow until a maximum is reached, and then moving downward along the descent flow to a (possibly different) local minimum. Iteration of this procedure gives rise to a bipartite digraph whose nodes are the local minima/maxima." @default.
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- W121626557 date "2004-01-01" @default.
- W121626557 modified "2023-09-25" @default.
- W121626557 title "Constrained Global Optimization: Adaptive Gradient Flows" @default.
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- W121626557 doi "https://doi.org/10.1007/978-1-4613-0251-3_12" @default.
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