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arXiv 2103.03628math.OC
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深半鞅最优输运

Deep Semi-Martingale Optimal Transport

Ivan Guo, Nicolas Langrené, Grégoire Loeper, Wei Ning

摘要:

我们提出了两种基于深度神经网络的方法来解决半鞅最优输运问题。第一种方法是基于终端约束的放松/惩罚,并使用深度神经网络来解决。第二种方法是基于对偶公式的问题,我们表示为一个鞍点问题,并使用对抗网络解决。这两种方法都是无网格的,因此减轻了维数灾难。我们测试我们的方法的性能和准确性的几个例子到10维。我们也适用于第一个算法的投资组合最优化问题的目标是,给定一个初始的财富分布,找到一个投资策略,导致规定的终端财富分布。

英文摘要:

We propose two deep neural network-based methods for solving semi-martingale optimal transport problems. The first method is based on a relaxation/penalization of the terminal constraint, and is solved using deep neural networks. The second method is based on the dual formulation of the problem, which we express as a saddle point problem, and is solved using adversarial networks. Both methods are mesh-free and therefore mitigate the curse of dimensionality. We test the performance and accuracy of our methods on several examples up to dimension 10. We also apply the first algorithm to a portfolio optimization problem where the goal is, given an initial wealth distribution, to find an investment strategy leading to a prescribed terminal wealth distribution.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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