# Sequential Convex Programming For Non-Linear Stochastic Optimal Control

arXiv: https://arxiv.org/abs/2009.05182 · HTML: https://mlsanity.com/paper/2009.05182/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

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

- [Non-Parametric Learning of Stochastic Differential Equations with Non-asymptotic Fast Rates of Convergence](https://mlsanity.com/paper/2305.15557/) (2023)
- [Convex Hulls of Reachable Sets](https://mlsanity.com/paper/2303.17674/) (2023)
- [Safely Learning Controlled Stochastic Dynamics](https://mlsanity.com/paper/2506.02754/) (2025)
- [Unified Optimality Conditions for Stochastic Optimal Control in the Rough Path and Ito Frameworks](https://mlsanity.com/paper/2609.38395/) (2026)

## Abstract (arXiv)

This work introduces a sequential convex programming framework for non-linear, finite-dimensional stochastic optimal control, where uncertainties are modeled by a multidimensional Wiener process. We prove that any accumulation point of the sequence of iterates generated by sequential convex programming is a candidate locally-optimal solution for the original problem in the sense of the stochastic Pontryagin Maximum Principle. Moreover, we provide sufficient conditions for the existence of at least one such accumulation point. We then leverage these properties to design a practical numerical method for solving non-linear stochastic optimal control problems based on a deterministic transcription of stochastic sequential convex programming.