Greedy Strategies for Convex Optimization
classification
🧮 math.NA
cs.NA
keywords
conditionsconvexfunctiongreedymodulusstrategiesalgorithmsapproximation
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We investigate two greedy strategies for finding an approximation to the minimum of a convex function $E$ defined on a Hilbert space $H$. We prove convergence rates for these algorithms under suitable conditions on the objective function $E$. These conditions involve the behavior of the modulus of smoothness and the modulus of uniform convexity of $E$.
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