Multi-policy improvement in stochastic optimization with forward recursive function criteria

Decision criteria in risk analysis : an application of stochastic dominance with respect to a function

Multi-criteria improvement of options

Designing the way a complex system should evolve to better match the customers’ requirements provides an interesting class of applications for muticriteria techniques. The required models to support the improvement design of a complex system must include both preference models and system behavioral models. A MAUT model captures the decisions related to customers’ preferences whereas a fuzzy rep...

Multi-Criteria Scheduling Optimization With Genetic Algorithms

Multi-criteria scheduling optimization with genetic algorithms is described. Scheduling optimization methodology provides the planner with a quick and efficient scheduling method and enables him/her to experiment and decide which of the suitable solutions will become the production plan. The scheduling system is composed of a business information system a database, a discrete event simulation m...

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

In this paper, we study and analyze the mini-batch version of StochAstic Recursive grAdient algoritHm (SARAH), a method employing the stochastic recursive gradient, for solving empirical loss minimization for the case of nonconvex losses. We provide a sublinear convergence rate (to stationary points) for general nonconvex functions and a linear convergence rate for gradient dominated functions,...

Stochastic global maximum principle for optimization with recursive utilities

In this paper, we study the recursive stochastic optimal control problems. The control domain does not need to be convex, and the generator of the backward stochastic differential equation can contain z. We obtain the variational equations for backward stochastic differential equations, and then obtain the maximum principle which solves completely Peng’s open problem.