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Primal-dual subgradient method for constrained convex optimization problems
- Source :
- Optimization Letters. 15:1491-1504
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
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.
- Subjects :
- Mathematical optimization
021103 operations research
Control and Optimization
Computer science
0211 other engineering and technologies
Regular polygon
010103 numerical & computational mathematics
02 engineering and technology
Lipschitz continuity
01 natural sciences
Dual (category theory)
Range (mathematics)
Optimization and Control (math.OC)
Simple (abstract algebra)
Convex optimization
FOS: Mathematics
Differentiable function
0101 mathematics
Mathematics - Optimization and Control
Subgradient method
Subjects
Details
- ISSN :
- 18624480 and 18624472
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- Optimization Letters
- Accession number :
- edsair.doi.dedup.....278882876eafc78dde486e3ff46fcd14