The Approximate Linear Programming (ALP) approach to value function approximation for MDPs is a parametric value function approximation method, in that it represents the value function as a linear combination of features which are chosen a priori. Choosing these features can be a difficult challenge in itself. One recent effort, Regularized Approximate Linear Programming (RALP), uses L1 regular...

Water resources sustainability is one of the major issues in the agricultural sustainability. In this study sustainability of water resources has been investigated by use of linear and non-linear models in six models based on optimal utilization of water resources in the north parts farms of Iran because of incorrect use of agricultural water resources, from 2011 to 2012. Also “gross margin per...

General idea of nonlinear correcting factors that was successfully applied to accelerate and derive algorithms for many linear and nonlinear problems is used to develop several efficient procedures for solving linear systems of equations based on the Jacobi algorithm. Results of the theoretical study and extensive computer experiments for stochastic matrices are presented and analyzed to determ...

‎linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints‎. ‎in this paper‎, ‎to solve this problem‎, ‎we combine a discretization method and a neural network method‎. ‎by a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem‎. ‎then‎, ‎we use...

In this contribution, genetic programming is combined with continuum regression to produce two novel non-linear continuum regression algorithms. The first is a ‘sequential’ algorithm while the second, adopts a ‘team-based’ strategy. Having discussed continuum regression, the modifications required to extend the algorithm for non-linear modelling are outlined. The results of applying the derived...