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Consider the problem of minimizing functions that are Lipschitz and strongly convex, but not necessarily differentiable.
A stochastic approximation method
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Earlier work this paper cites.
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David A. Freedman · 1975
Earlier work this paper cites.
Problem complexity and method efficiency in optimization
A. S. Nemirovsky and D. B. Yudin · 1983
Earlier work this paper cites.
Efficient estimations from a slowly convergent Robbins-Monro process
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Earlier work this paper cites.
Acceleration of stochastic approximation by averaging
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Earlier work this paper cites.
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Earlier work this paper cites.
Fast approximation algorithms for fractional packing and covering problems
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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