Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Jia, Xiaoxi"'
Autor:
Jia, Xiaoxi, Wang, Kai
The proximal gradient method is a standard approach to solve the composite minimization problems where the objective function is the sum of a continuously differentiable function and a lower semicontinuous, extended-valued function. For both monotone
Externí odkaz:
http://arxiv.org/abs/2411.19256
Autor:
Jia, Xiaoxi
This thesis, first, is devoted to the theoretical and numerical investigation of an augmented Lagrangian method for the solution of optimization problems with geometric constraints, subsequently, as well as constrained structured optimization problem
We consider a composite optimization problem where the sum of a continuously differentiable and a merely lower semicontinuous function has to be minimized. The proximal gradient algorithm is the classical method for solving such a problem numerically
Externí odkaz:
http://arxiv.org/abs/2301.05002
Publikováno v:
Math. Program. (2023)
We investigate finite-dimensional constrained structured optimization problems, featuring composite objective functions and set-membership constraints. Offering an expressive yet simple language, this problem class provides a modeling framework for a
Externí odkaz:
http://arxiv.org/abs/2203.05276
This paper is devoted to the theoretical and numerical investigation of an augmented Lagrangian method for the solution of optimization problems with geometric constraints. Specifically, we study situations where parts of the constraints are nonconve
Externí odkaz:
http://arxiv.org/abs/2105.08317
Autor:
De Marchi, Alberto1 (AUTHOR) alberto.demarchi@unibw.de, Jia, Xiaoxi2 (AUTHOR), Kanzow, Christian2 (AUTHOR), Mehlitz, Patrick3 (AUTHOR)
Publikováno v:
Mathematical Programming. Sep2023, Vol. 201 Issue 1/2, p863-896. 34p.
Autor:
Jia, Xiaoxi1 (AUTHOR), Kanzow, Christian1 (AUTHOR), Mehlitz, Patrick2,3 (AUTHOR) mehlitz@b-tu.de, Wachsmuth, Gerd2 (AUTHOR)
Publikováno v:
Mathematical Programming. May2023, Vol. 199 Issue 1/2, p1365-1415. 51p.
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Publikováno v:
Optimization; Oct2022, Vol. 71 Issue 10, p2819-2839, 21p