An ADMM-based SQP method for separably smooth nonconvex optimization
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Publication:2069362
DOI10.1186/s13660-020-02347-3zbMath1503.65125OpenAlexW3030981336MaRDI QIDQ2069362
Publication date: 20 January 2022
Published in: Journal of Inequalities and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1186/s13660-020-02347-3
Numerical mathematical programming methods (65K05) Convex programming (90C25) Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30) Numerical optimization and variational techniques (65K10)
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Cites Work
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- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Fast alternating linearization methods for minimizing the sum of two convex functions
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- A superlinearly convergent implicit smooth SQP algorithm for mathematical programs with nonlinear complementarity constraints
- An efficient feasible SQP algorithm for inequality constrained optimization
- A new superlinearly convergent norm-relaxed method of strongly sub-feasible direction for inequality constrained optimization
- Global convergence of an SQP method without boundedness assumptions on any of the iterative sequences
- A dual algorithm for the solution of nonlinear variational problems via finite element approximation
- Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods
- On non-ergodic convergence rate of Douglas-Rachford alternating direction method of multipliers
- On the $O(1/n)$ Convergence Rate of the Douglas–Rachford Alternating Direction Method
- A Strictly Contractive Peaceman--Rachford Splitting Method for Convex Programming
- Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems
- Convergence Analysis of a Proximal-Like Minimization Algorithm Using Bregman Functions
- Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
- Global Convergence of Splitting Methods for Nonconvex Composite Optimization
- Clarke Subgradients of Stratifiable Functions
- A successive quadratic programming algorithm with global and superlinear convergence properties
- A Superlinearly Convergent Feasible Method for the Solution of Inequality Constrained Optimization Problems
- Fast Alternating Direction Optimization Methods
- Iteration-Complexity of Block-Decomposition Algorithms and the Alternating Direction Method of Multipliers
- The Łojasiewicz Inequality for Nonsmooth Subanalytic Functions with Applications to Subgradient Dynamical Systems
- A quadratically-convergent algorithm for general nonlinear programming problems
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