Solving monotone stochastic variational inequalities and complementarity problems by progressive hedging
MetadataShow full item record
The final publication is available at Springer via http://dx.doi.org/10.1007/s10107-018-1251-y
The concept of a stochastic variational inequality has recently been articulated in a new way that is able to cover, in particular, the optimality conditions for a multistage stochastic programming problem. One of the long-standing methods for solving such an optimization problem under convexity is the progressive hedging algorithm. That approach is demonstrated here to be applicable also to solving multistage stochastic variational inequality problems under monotonicity, thus increasing the range of applications for progressive hedging. Stochastic complementarity problems as a special case are explored numerically in a linear two-stage formulation.
Showing items related by title, author, creator and subject.
Liu, Chunmin (2008)The optimization problems involving stochastic systems are often encountered in financial systems, networks design and routing, supply-chain management, actuarial science, telecommunications systems, statistical pattern ...
Design exploration with stochastic models of variation: Comparing two examples from facade subdivisionDatta, Sambit (2013)The imitation of natural processes in architectural design is a long-standing area of research in computational design. The approach of “directed randomness” permits the stochastic exploration of a vast space of design ...
Stochastic Optimization over a Pareto Set Associated with a Stochastic Multi-Objective Optimization ProblemBonnel, Henri; Collonge, J. (2014)We deal with the problem of minimizing the expectation of a real valued random function over the weakly Pareto or Pareto set associated with a Stochastic Multi-objective Optimization Problem, whose objectives are expectations ...