A novel method of solving a quadratic programming problem under stochastic conditions

Abstract

The optimization problem calculates an accurate solution, and the result of inequality constraints results in an approximate solution. The new method is developed under stochastic conditions to give an optimal expected value for the linear programming problem of downscaling data generated through an autoregressive integrated moving average (ARIMA) model. In this chapter, we predict future values using the ARIMA model for specified optimization problems. Wolfe’s modified method is adopted to solve the linear programming problem under stochastic conditions.