Random Sampling with Removal

We study randomized algorithms for constrained optimization, in abstract frameworks that include, in strictly increasing generality: convex programming; LP-type problems; violator spaces; and a setting we introduce, consistent spaces . Such algorithms typically involve a step of finding the optimal...

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Veröffentlicht in:	Discrete & computational geometry 2020-10, Vol.64 (3), p.700-733
Hauptverfasser:	Clarkson, Kenneth L., Gärtner, Bernd, Lengler, Johannes, Szedlák, May
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Combinatorics Computational geometry Computational Mathematics and Numerical Analysis Constraints Convexity Lower bounds Mathematical programming Mathematics Mathematics and Statistics Optimization Random sampling Ricky Pollack Memorial Issue Upper bounds
Online-Zugang:	Volltext
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