Improved Algorithms for Convex Minimization in Relative Scale.
SIAM JOURNAL ON OPTIMIZATION(2011)
摘要
In this paper we propose two modifications to Nesterov's algorithms for minimizing convex functions in relative scale. The first is based on a bisection technique and leads to improved theoretical iteration complexity, and the second is a heuristic for avoiding restarting behavior. The fastest of our algorithms produces a solution within relative error O(1/k) of the optimum, with k being the iteration counter.
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关键词
convex optimization,relative scale,sublinearity,Nesterov's smoothing technique,Lowner-John ellipsoids
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