Abstract We consider stochastic problems in which both the objective function and feasible set are affected by uncertainty. address these using a K -adaptability approach, solutions for given problem computed before uncertainty dissolves afterwards best of them can be chosen realized scenario. analyze complexity resulting from theoretical viewpoint, showing that, even case deterministic solved ...

General stochastic query optimization (GSQO) problem for multiple join — join of p relations which are stored at p different sites — is presented. GSQO problem leads to a special kind of nonlinear programming problem (P ). Problem (P ) is solved by using a constructive method. A sequence converging to the solution of the optimization problem is built. Two algorithms for solving optimization pro...

Stochastic optimization is an important task in many optimization problems where the tasks are not expressible as convex optimization problems. In the case of non-convex optimization problems, various different stochastic algorithms like simulated annealing, evolutionary algorithms, and tabu search are available. Most of these algorithms require user-defined parameters specific to the problem i...

The advent of competitive markets confronts each producer with the problem of optimally allocating his energy/capacity so as to maximize his pro$ts. The multiplicity of auctions in electricity markets and the nontrivial constraints imposed by technical and bidding rules make the problem of crucial importance and di/cult to model and solve. Further di/culties are represented by the dynamic and s...

This paper outlines, and through stylized examples evaluates a novel highly effective computational technique in quantitative finance. Empirical Risk Minimization (ERM) neural networks are key to this approach. Powerful open source optimization libraries allow for efficient implementations of algorithm making it viable high-dimensional structures. The free-boundary problems related American Ber...

Optimization under Uncertainty Optimization under Uncertainty: An Overview Urmila Diwekar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Two-Stage Stochastic Linear Programming: A Tutorial Vicente Rico-Ramirez . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Combinatorial Optimization under Uncertainty Ki-Joo Kim . . . . . . . . . . . . . . . . . . . . . . . . . . ...

This paper presents an optimization strategy for a broke system design and operation in papermaking process. A multiobjective stochastic optimization model is presented featuring (i) a stochastic two-state Markov process based submodel for the broke tower, (ii) an operational submodel for the optimization of the broke dosage, and (iii) a multiobjective design problem. An efficient optimization ...