Solving Stochastic Programming Problems via Kalman Filter and Affine Scaling

Kalman filtering theory [1] has been around for more than two decades and has been the backbone of modern applied stochastic estimation. It is used extensively in many areas including engineering and econometrics. A Kalman filter estimates the states of a system from observations made about the stochastic inputs and outputs of the system. The Kalman filter performs its operations in an attractive recursive fashion, and can be easily implemented on a computer.