Tuesday, 15:45 - 16:10 h, Room: MA 144


Eugenio Mijangos
An algorithm for nonlinearly-constrained nonlinear two-stage stochastic problems


We put forward an algorithm to solve nonlinearly-constrained two-stage stochastic problems with a nonlinear objective function. It is based on the Twin Node Family (TNF) concept involved in the Branch-and-Fix Coordination method. These problems have continuous and binary variables in the first stage and only continuous variables in the second stage. The nonanticipativity constraints are fulfilled by
TNF strategy.
In this work, given that the objective function is nonlinear, we propose to solve each nonlinear subproblem generated in the nodes of the trees associated with this method by solving sequences of quadratic subproblems. If the nonlinear constraints are convex we approximate them by means of outer linear approximations; otherwise, we relax these constraints by using augmented Lagrangian techniques. These methods have been implemented in C++ with the help of Cplex 12.1 to solve only the quadratic approximations. The test problems have been randomly generated by using a C++ code developed by this author. Numerical experiments have been performed and its efficiency has been compared with that of BONMIN (COIN-OR). Results are promising.


Talk 2 of the contributed session Tue.3.MA 144
"Nonlinear stochastic optimization" [...]
Cluster 22
"Stochastic optimization" [...]


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