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1111 r"""
1212 :class:`~torchjd.aggregation._aggregator_bases.Aggregator` performing the gradient aggregation
1313 step of `Multiple-gradient descent algorithm (MGDA) for multiobjective optimization
14- <https://www.sciencedirect.com/science/article/pii/S1631073X12000738>`_. The implementation is
15- based on Algorithm 2 of `Multi-Task Learning as Multi-Objective Optimization
14+ <https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/j.crma.2012.03.014/>`_.
15+ The implementation is based on Algorithm 2 of `Multi-Task Learning as Multi-Objective
16+ Optimization
1617 <https://proceedings.neurips.cc/paper_files/paper/2018/file/432aca3a1e345e339f35a30c8f65edce-Paper.pdf>`_.
1718
1819 :param epsilon: The value of :math:`\hat{\gamma}` below which we stop the optimization.
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