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Scaled Proximal Gradient Methods for Multiobjective Optimization: Improved Linear Convergence and Nesterov's Acceleration
Jian Chen, Liping Tang, Xinmin Yang
Over the past two decades, descent methods have received substantial attention within the multiobjective optimization field. Nonetheless, both theoretical analyses and empirical ev…
Generalized conditional gradient methods for multiobjective composite optimization problems with H{ö}lder condition
Wang Chen, Liping Tang, Xinmin Yang
In this paper, we deal with multiobjective composite optimization problems, where each objective function is a combination of smooth and possibly non-smooth functions. We first pro…
Mirror descent method for stochastic multi-objective optimization
Linxi Yang, Liping Tang, Jiahao Lv +2
Stochastic multi-objective optimization (SMOO) has recently emerged as a powerful framework for addressing machine learning problems with multiple objectives. The bias introduced b…
First-order Methods for Unconstrained Vector Optimization Problems: A Unified Majorization-Minimization Perspective
Jian Chen, Jingjie Liu, Liping Tang +1
In this paper, we develop a unified majorization-minimization scheme and convergence analysis with first-order surrogate functions for unconstrained vector optimization problems (V…
A Subspace Minimization Barzilai-Borwein Method for Multiobjective Optimization Problems
Jian Chen, Liping Tang. Xinmin Yang
Nonlinear conjugate gradient methods have recently garnered significant attention within the multiobjective optimization community. These methods aim to maintain consistency in con…
On the convergence of conditional gradient method for unbounded multiobjective optimization problems
Wang Chen, Yong Zhao, Liping Tang +1
This paper focuses on developing a conditional gradient algorithm for multiobjective optimization problems with an unbounded feasible region. We employ the concept of recession con…