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The problem of data representation on a sphere of unknown radius arises from various disciplines such as Statistics (spatial data representation), Psychology (constrained multidi-mensional scaling), and Computer Science (machine learning and pattern recognition). The best representation often needs to minimize a distance function of the data on a sphere as(More)
s Towards optimal Newton-type methods for nonconvex smooth optimization Coralia Cartis We show that the steepest-descent and Newton methods for unconstrained non-convex optimization, under standard assumptions, may both require a number of iterations and function evaluations arbitrarily close to the steepest-descent's global worst-case complexity bound.(More)
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