Pareto Optimal Solutions for Stochastic Dynamic Programming Problems via Monte Carlo Simulation

Stochastic Programming by Monte Carlo Simulation Methods

We consider in this paper stochastic programming problems which can be formulated as an optimization problem of an expected value function subject to deterministic constraints. We discuss a Monte Carlo simulation approach based on sample average approximations to a numerical solution of such problems. In particular, we give a survey of a statistical inference of the sample average estimators of...

Pareto-optimal Solutions for Multi-objective Optimal Control Problems using Hybrid IWO/PSO Algorithm

Heuristic optimization provides a robust and efficient approach for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier. The convergence rate and suitable diversity of solutions are of great importance for multi-objective evolutionary algorithms. The focu...

Efficient Monte Carlo simulations for stochastic programming

This paper is concerned with chance constrained programming to deal with nonlinear optimization problems with random parameters. Specific Monte Carlo methods to evaluate the gradient and Hessian of probabilistic constraints are proposed and discussed. These methods are implemented in penalization optimization routines adapted to stochastic optimization. They are shown to reduce the computationa...

Stochastic Simulation,monte Carlo Methods and Applications1

Efficient Monte Carlo Simulation with Stochastic Volatility

Monte Carlo simulation is a powerful aid in many fields. In this thesis it is used for pricing of financial derivatives. Achieving accurate results with Monte Carlo is rather timeconsuming due to its slow convergence. However, there are ways to improve the accuracy of each simulation, for instance by reducing the inevitable discretization bias. In our financial context, this concerns the discre...