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Analysis

This paper addresses a challenging class of multiobjective optimization problems involving non-smooth and non-convex objective functions. The authors propose a proximal subgradient algorithm and prove its convergence to stationary solutions under mild assumptions. This is significant because it provides a practical method for solving a complex class of optimization problems that arise in various applications.
Reference

Under mild assumptions, the sequence generated by the proposed algorithm is bounded and each of its cluster points is a stationary solution.