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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… Click to show full abstract
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 in Math Programm 120(1): 221–259, 2009) in this setting and prove that it is an optimal method.
Keywords:
convex optimization;
optimization;
dual subgradient;
primal dual;
method;
optimization problems
Journal Title: Optimization Letters
Year Published: 2021
Link to full text (if available)
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Journal Title: Optimization Letters
Year Published: 2021
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!