# Distributionally Robust Geometric Joint Chance-Constrained Optimization: Neurodynamic Approaches

arXiv: https://arxiv.org/abs/2603.06597 · HTML: https://mlsanity.com/paper/2603.06597/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 65 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [GAIT: A Geometric Approach to Information Theory](https://mlsanity.com/paper/1906.08325/) (2019)
- [Distributionally Robust Optimization with Correlated Data from Vector Autoregressive Processes](https://mlsanity.com/paper/1909.03433/) (2019)

## Abstract (arXiv)

This paper proposes a two-time scale neurodynamic duplex approach to solve distributionally robust geometric joint chance-constrained optimization problems. The probability distributions of the row vectors are not known in advance and belong to a certain distributional uncertainty set. In our paper, we study three uncertainty sets for the unknown distributions. The neurodynamic duplex is designed based on three projection equations. The main contribution of our work is to propose a neural network-based method to solve distributionally robust joint chance-constrained optimization problems that converges in probability to the global optimum without the use of standard state-of-the-art solving methods. We show that neural networks can be used to solve multiple instances of a problem. In the numerical experiments, we apply the proposed approach to solve a problem of shape optimisation and a telecommunication problem.