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Primal-dual subgradient method for constrained convex optimization problems

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 نشر من قبل Michael Metel R
 تاريخ النشر 2020
  مجال البحث
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This paper considers a general convex constrained problem setting where functions are not assumed to be differentiable nor Lipschitz continuous. Our motivation is in finding a simple first-order method for solving a wide range of convex optimization problems with minimal requirements. We study the method of weighted dual averages (Nesterov, 2009) in this setting and prove that it is an optimal method.



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