A heuristic procedure, called successive regression approximations (SRA) has been developed for solving stochastic programming problems. They range from equation solving to probabilistic constrained and two‐stage models through a combined model of Prékopa. We show here, that due to enhancements in the computer program, SRA can be used to solve large‐scale two‐stage problems with 100 first stage decision variables and a 120 dimensional normally distributed random right hand side vector in the second stage problem. A FORTRAN source program and computational results for 124 problems are presented at http://www.uni-corvinus.hu/~ideak1.
