# (Martingale) Optimal Transport And Anomaly Detection With Neural Networks: A Primal-dual Algorithm

## TL;DR

This paper presents a primal-dual algorithm for martingale optimal transport problems, with applications to anomaly detection and financial data generation, leveraging cost functions similar to those used in training GANs.

## Contribution

It introduces a novel primal-dual algorithm for martingale optimal transport problems with practical applications in anomaly detection and financial data synthesis.

## Key findings

- Effective algorithm for martingale optimal transport
- Successful application to anomaly detection tasks
- Generates realistic financial data samples

## Abstract

In this paper, we introduce a primal-dual algorithm for solving (martingale) optimal transportation problems, with cost functions satisfying the twist condition, close to the one that has been used recently for training generative adversarial networks. As some additional applications, we consider anomaly detection and automatic generation of financial data.