Stochastic nonlinear complementarity problems: stochastic programming reformulation and penalty-based approximation method

DOI10.1007/s10957-009-9606-4zbMath1196.90117OpenAlexW2068042327WikidataQ57932015 ScholiaQ57932015MaRDI QIDQ965063

B. E. Eshmatov

Publication date: 21 April 2010

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10957-009-9606-4

zbMATH Keywords

penalty methodoptimization reformulationsample average approximationStochastic complementarity problems

Mathematics Subject Classification ID

Stochastic programming (90C15) Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming) (90C33)

Related Items (10)

The deterministic ERM and CVaR reformulation for the stochastic generalized complementarity problem ⋮ A smoothing Levenberg-Marquardt algorithm for solving a class of stochastic linear complementarity problem ⋮ A sample average approximation method based on a D-gap function for stochastic variational inequality problems ⋮ CVaR-constrained stochastic programming reformulation for stochastic nonlinear complementarity problems ⋮ An implementable SAA nonlinear Lagrange algorithm for constrained minimax stochastic optimization problems ⋮ A smoothing Newton method for solving a class of stochastic linear complementarity problems ⋮ Nonsmooth Levenberg-Marquardt type method for solving a class of stochastic linear complementarity problems with finitely many elements ⋮ Polymorphic uncertain nonlinear programming approach for maximizing the capacity of V-belt driving ⋮ A smooth penalty-based sample average approximation method for stochastic complementarity problems ⋮ The distributionally robust complementarity problem

Cites Work

• Unnamed Item
• Stochastic nonlinear complementarity problem and applications to traffic equilibrium under uncertainty
• Solving stochastic mathematical programs with equilibrium constraints via approximation and smoothing implicit programming with penalization
• Robust solution of monotone stochastic linear complementarity problems
• Sample-path solution of stochastic variational inequalities
• Stochastic programming with equilibrium constraints
• Convergence theory for nonconvex stochastic programming with an application to mixed logit
• Combined Monte Carlo sampling and penalty method for Stochastic nonlinear complementarity problems
• Stochastic $R_0$ Matrix Linear Complementarity Problems
• Analysis of Sample-Path Optimization
• Finite-Dimensional Variational Inequalities and Complementarity Problems
• Expected Residual Minimization Method for Stochastic Linear Complementarity Problems
• Convergence Analysis of Sample Average Approximation Methods for a Class of Stochastic Mathematical Programs with Equality Constraints
• New restricted NCP functions and their applications to stochastic NCP and stochastic MPEC
• New reformulations for stochastic nonlinear complementarity problems
• A Regularized Sample Average Approximation Method for Stochastic Mathematical Programs with Nonsmooth Equality Constraints

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