# On the Convergence of the Iterative Linear Exponential Quadratic Gaussian Algorithm to Stationary Points

arXiv: https://arxiv.org/abs/1910.08221 · HTML: https://mlsanity.com/paper/1910.08221/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity](https://mlsanity.com/paper/2101.01041/) (2021)
- [RAP: Risk-Aware Prediction for Robust Planning](https://mlsanity.com/paper/2210.01368/) (2022)
- [Interacting Particle Systems for Fast Linear Quadratic RL](https://mlsanity.com/paper/2406.11057/) (2024)
- [Power Homotopy for Zeroth-Order Non-Convex Optimizations](https://mlsanity.com/paper/2511.13592/) (2025)

## Abstract (arXiv)

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.