论文标题
遵循双配音器：多目标优化的简单方法
Follow the bisector: a simple method for multi-objective optimization
论文作者
论文摘要
这项研究提出了一种新型的等方向方法（EDM），以解决多目标优化问题。我们考虑了优化问题，其中必须最小化多个可区分的损失。提出的方法计算每种迭代中的下降方向，以确保目标函数的相对相对相对相对减少。该下降方向基于个体损失的归一化梯度。因此，解决多尺度损失的多目标优化问题是适当的。我们在使用标准数据集的情况下测试了有关分类问题和多任务学习问题的建议方法。将EDM与解决这些问题的其他方法进行了比较。
This study presents a novel Equiangular Direction Method (EDM) to solve a multi-objective optimization problem. We consider optimization problems, where multiple differentiable losses have to be minimized. The presented method computes descent direction in every iteration to guarantee equal relative decrease of objective functions. This descent direction is based on the normalized gradients of the individual losses. Therefore, it is appropriate to solve multi-objective optimization problems with multi-scale losses. We test the proposed method on the imbalanced classification problem and multi-task learning problem, where standard datasets are used. EDM is compared with other methods to solve these problems.
