  A classical method for risk-sensitive nonlinear control is the iterative
linear exponential quadratic Gaussian algorithm. We present its convergence
analysis from a first-order optimization viewpoint. We identify the objective
that the algorithm actually minimizes and we show how the addition of a
proximal term guarantees convergence to a stationary point.
