The augmented Lagrangian method for a type of inverse quadratic programming problems over second-order cones
From MaRDI portal
Publication:458938
DOI10.1007/s11750-011-0227-3zbMath1298.90005OpenAlexW2037889019MaRDI QIDQ458938
Yi Zhang, Yue Wu, Li-wei Zhang
Publication date: 8 October 2014
Published in: Top (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11750-011-0227-3
rate of convergenceaugmented Lagrangian methodinverse optimizationdamped semismooth Newton methodsecond-order cone quadratic programming
Quadratic programming (90C20) Computational methods for problems pertaining to operations research and mathematical programming (90-08)
Uses Software
Cites Work
- Unnamed Item
- Generalized Hessian matrix and second-order optimality conditions for problems with \(C^{1,1}\) data
- Generalized derivatives and nonsmooth optimization, a finite dimensional tour (with comments and rejoinder)
- The rate of convergence of the augmented Lagrangian method for nonlinear semidefinite programming
- A smoothing Newton method for a type of inverse semi-definite quadratic programming problem
- An augmented Lagrangian method for a class of Inverse quadratic programming problems
- Improving directions of negative curvature in an efficient manner
- Applications of second-order cone programming
- On an instance of the inverse shortest paths problem
- A further study on inverse linear programming problems
- Solution structure of some inverse combinatorial optimization problems
- An augmented Lagrangian interior-point method using directions of negative curvature
- Second-order cone programming
- Some reverse location problems
- Inverse conic programming with applications
- The complexity analysis of the inverse center location problem
- On concepts of directional differentiability
- Inverse combinatorial optimization: a survey on problems, methods, and results
- Calculating some inverse linear programming problems
- A nonsmooth version of Newton's method
- Multiplier and gradient methods
- The multiplier method of Hestenes and Powell applied to convex programming
- Semismoothness of solutions to generalized equations and the Moreau-Yosida regularization
- Combined Primal-Dual and Penalty Methods for Constrained Minimization
- Inverse Optimization
- Optimization and nonsmooth analysis
- On the method of multipliers for convex programming
- On Penalty and Multiplier Methods for Constrained Minimization
- Semismooth and Semiconvex Functions in Constrained Optimization
- A dual approach to solving nonlinear programming problems by unconstrained optimization
- Combinatorial algorithms for inverse network flow problems
- Finite-Dimensional Variational Inequalities and Complementarity Problems
- Semismooth Homeomorphisms and Strong Stability of Semidefinite and Lorentz Complementarity Problems
- Some Properties of the Augmented Lagrangian in Cone Constrained Optimization