2020/07/14 by Alexandr Katrutsa, Daniil Merkulov, Katrutsa, Alexandr +5 · 1 citation
Computer Science · #Advanced Multi-Objective Optimization Algorithms #Machine Learning and Data Classification #Metaheuristic Optimization Algorithms Research
paper · pdf · doi:10.48550/arxiv.2007.06937
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.