Applications of the method of partial inverses to convex programming: Decomposition
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Publication:3680618
DOI10.1007/BF01586091zbMath0565.90058MaRDI QIDQ3680618
Publication date: 1985
Published in: Mathematical Programming (Search for Journal in Brave)
linear convergenceproximal point algorithmseparable convex programmingmonotone multifunctionprimal-dual decomposition method
Numerical mathematical programming methods (65K05) Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Methods of successive quadratic programming type (90C55)
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Cites Work
- Unnamed Item
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- Unnamed Item
- Unnamed Item
- Partial inverse of a monotone operator
- Auxiliary problem principle and decomposition of optimization problems
- A primal-dual projection method for solving systems of linear inequalities
- Monotone (nonlinear) operators in Hilbert space
- A dual algorithm for the solution of nonlinear variational problems via finite element approximation
- Decomposition in large system optimization using the method of multipliers
- Convexification procedures and decomposition methods for nonconvex optimization problems
- A projection method for least-squares solutions to overdetermined systems of linear inequalities
- The use of Hestenes' method of multipliers to resolve dual gaps in engineering system optimization
- Multiplier and gradient methods
- On the maximal monotonicity of subdifferential mappings
- The multiplier method of Hestenes and Powell applied to convex programming
- Decomposition Principle for Linear Programs
- Asymptotic Convergence Analysis of the Proximal Point Algorithm
- Generalized Lagrange Multiplier Method for Solving Problems of Optimum Allocation of Resources
- Fixed and Variable Constraints in Sensitivity Analysis
- Perturbed Kuhn-Tucker points and rates of convergence for a class of nonlinear-programming algorithms
- Monotone Operators and the Proximal Point Algorithm
- Sensitivity analysis for nonlinear programming using penalty methods
- Optimization by decomposition and coordination: A unified approach
- Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
- Convex Analysis
- Primal Resource-Directive Approaches for Optimizing Nonlinear Decomposable Systems
- Duality in Nonlinear Programming: A Simplified Applications-Oriented Development