Feasible semismooth Newton method for a class of stochastic linear complementarity problems
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Publication:1014020
DOI10.1007/s10957-008-9406-2zbMath1191.90085OpenAlexW2030824567MaRDI QIDQ1014020
Publication date: 24 April 2009
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/20.500.11937/21687
constrained minimizationstochastic linear complementarity problemsfeasible semismooth Newton methods
Methods of quasi-Newton type (90C53) Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming) (90C33)
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Cites Work
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- On NCP-functions
- Solution of monotone complementarity problems with locally Lipschitzian functions
- A feasible semismooth asymptotically Newton method for mixed complementarity problems
- A penalized Fischer-Burmeister NCP-function
- Feasible descent algorithms for mixed complementarity problems
- Stochastic $R_0$ Matrix Linear Complementarity Problems
- Optimization and nonsmooth analysis
- A special newton-type optimization method
- Newton and Quasi-Newton Methods for a Class of Nonsmooth Equations and Related Problems
- Convergence Analysis of Some Algorithms for Solving Nonsmooth Equations
- Finite-Dimensional Variational Inequalities and Complementarity Problems
- Expected Residual Minimization Method for Stochastic Linear Complementarity Problems
- New restricted NCP functions and their applications to stochastic NCP and stochastic MPEC
- New reformulations for stochastic nonlinear complementarity problems
- Strictly feasible equation-based methods for mixed complementarity problems
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