8 citations · 13 across the 38 of their papers we have counts for
39 papers
A Tight Surrogate-Based Adaptive Step Size for the Multiobjective Conditional Gradient Method
Wang Chen, Yong Zhao, Liping Tang +1
The multiobjective conditional gradient (MCondG) method is an effective descent-type algorithm for solving constrained multiobjective optimization problems. In this algorithm, the…
Second-order Methods for Multiobjective Composite Optimization: Preconditioning Strategies, Subspace Variants and Inexact Solutions
Jian Chen, Xinmin Yang
Multiobjective composite optimization problems arise in sparse regularization, constrained multiobjective models, and multi-task learning, but their numerical solution remains chal…
Preconditioned Proximal Gradient Methods with Conjugate Momentum: A Subspace Perspective
Jian Chen, Xinmin Yang
In this paper, we propose a descent method for composite optimization problems with linear operators. Specifically, we first design a structure-exploiting preconditioner tailored t…
Barzilai-Borwein Diagonal Quasi-Newton Method for Nonconvex Multiobjective Optimization Problems
Hua Liu, Zhuoxin Fan, Liping Tang +1
This paper addresses the challenge of developing efficient algorithms for large-scale nonconvex multiobjective optimization problems (MOPs). While quasi-Newton methods are effectiv…
An accelerated primal-dual flow for linearly constrained multiobjective optimization
Hao Luo, Qiaoyuan Shu, Xinmin Yang
In this paper, we propose a continuous-time primal-dual approach for linearly constrained multiobjective optimization problems. A novel dynamical model, called accelerated multiobj…
An accelerated gradient method with adaptive restart for convex multiobjective optimization problems
Hao Luo, Liping Tang, Xinmin Yang
In this work, based on the continuous time approach, we propose an accelerated gradient method with adaptive residual restart for convex multiobjective optimization problems. For t…
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…
Gradient-based algorithms for multi-objective bi-level optimization
Xinmin Yang, Wei Yao, Haian Yin +2
Multi-Objective Bi-Level Optimization (MOBLO) addresses nested multi-objective optimization problems common in a range of applications. However, its multi-objective and hierarchica…
Level proximal subdifferential, variational convexity, and pointwise quadratic approximation
Honglin Luo, Xianfu Wang, Ziyuan Wang +1
Level proximal subdifferential was introduced by Rockafellar recently for studying proximal mappings of possibly nonconvex functions. In this paper a systematic study of level prox…
Last-iterate convergence of modified predictive method via high-resolution differential equation on bilinear game
Keke Li, Xinmin Yang
This paper discusses the convergence of the modified predictive method (MPM) proposed by Liang and stokes corresponding to high-resolution differential equations (HRDE) in bilinear…
A Branch and Bound Algorithm for Multiobjective Optimization Problems Using General Ordering Cones
Weitian Wu, Xinmin Yang
Many existing branch and bound algorithms for multiobjective optimization problems require a significant computational cost to approximate the entire Pareto optimal solution set. I…
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…
Obtaining properly Pareto optimal solutions of multiobjective optimization problems via the branch and bound method
Weitian Wu, Xinmin Yang
In multiobjective optimization, most branch and bound algorithms provide the decision maker with the whole Pareto front, and then decision maker could select a single solution fina…
Global convergence of block proximal iteratively reweighted algorithm with extrapolation
Jie Zhang, Xinmin Yang
In this paper, we propose a proximal iteratively reweighted algorithm with extrapolation based on block coordinate update aimed at solving a class of optimization problems which is…
The convergence rate of the accelerated proximal gradient algorithm for Multiobjective Optimization is faster than
Jie Zhang, Xinmin Yang
In this paper, we propose a fast proximal gradient algorithm for multiobjective optimization, it is proved that the convergence rate of the accelerated algorithm for multiobjective…
Conjugate gradient methods without line search for multiobjective optimization
Wang Chen, Yong Zhao, Liping Tang +1
This paper addresses unconstrained multiobjective optimization problems where two or more continuously differentiable functions have to be minimized. We delve into the conjugate gr…
Improvements to steepest descent method for multi-objective optimization
Wang Chen, Liping Tang, Xinmin Yang
In this paper, we propose a simple yet efficient strategy for improving the multi-objective steepest descent method proposed by Fliege and Svaiter (Math Methods Oper Res, 2000, 3:…
Learning to optimize by multi-gradient for multi-objective optimization
Linxi Yang, Xinmin Yang, Liping Tang
The development of artificial intelligence (AI) for science has led to the emergence of learning-based research paradigms, necessitating a compelling reevaluation of the design of…
Barzilai-Borwein Descent Methods for Multiobjective Optimization Problems with Variable Trade-off Metrics
Jian Chen, Liping Tang, Xinmin Yang
The imbalances and conditioning of the objective functions influence the performance of first-order methods for multiobjective optimization problems (MOPs). The latter is related t…
On the Convergence of Newton-type Proximal Gradient Method for Multiobjective Optimization Problems
Jian Chen, Xiaoxue Jiang, Liping Tang +1
In a recent study, Ansary (Optim Methods Softw 38(3):570-590,2023) proposed a Newton-type proximal gradient method for nonlinear multiobjective optimization problems (NPGMO). Howev…