An accelerated proximal gradient method for multiobjective optimization
This paper presents an accelerated proximal gradient method for multiobjective
optimization, in which each objective function is the sum of a continuously differentiable …
optimization, in which each objective function is the sum of a continuously differentiable …
[HTML][HTML] Efficient hybrid conjugate gradient techniques for vector optimization
Scalarization approaches transform vector optimization problems (VOPs) into single-
objective optimization but have trade-offs: information loss, subjective weight assignments …
objective optimization but have trade-offs: information loss, subjective weight assignments …
Convergence rates analysis of a multiobjective proximal gradient method
Many descent algorithms for multiobjective optimization have been developed in the last two
decades. Tanabe et al.(Comput Optim Appl 72 (2): 339–361, 2019) proposed a proximal …
decades. Tanabe et al.(Comput Optim Appl 72 (2): 339–361, 2019) proposed a proximal …
A generalized conditional gradient method for multiobjective composite optimization problems
This article deals with multiobjective composite optimization problems that consist of
simultaneously minimizing several objective functions, each of which is composed of a …
simultaneously minimizing several objective functions, each of which is composed of a …
On the convergence analysis of a proximal gradient method for multiobjective optimization
X Zhao, D Ghosh, X Qin, C Tammer, JC Yao - TOP, 2024 - Springer
We propose a proximal gradient method for unconstrained nondifferentiable multiobjective
optimization problems with the objective function being the sum of a proper lower …
optimization problems with the objective function being the sum of a proper lower …
A subspace inertial method for derivative-free nonlinear monotone equations
We introduce a subspace inertial line search algorithm (SILSA), for finding solutions of
nonlinear monotone equations (NME). At each iteration, a new point is generated in a …
nonlinear monotone equations (NME). At each iteration, a new point is generated in a …
Conditional gradient method for vector optimization
W Chen, X Yang, Y Zhao - Computational Optimization and Applications, 2023 - Springer
In this paper, we propose a conditional gradient method for solving constrained vector
optimization problems with respect to a partial order induced by a closed, convex and …
optimization problems with respect to a partial order induced by a closed, convex and …
A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective convex optimization
Convex-composite optimization, which minimizes an objective function represented by the
sum of a differentiable function and a convex one, is widely used in machine learning and …
sum of a differentiable function and a convex one, is widely used in machine learning and …
A descent method for nonsmooth multiobjective optimization in Hilbert spaces
The efficient optimization method for locally Lipschitz continuous multiobjective optimization
problems from Gebken and Peitz (J Optim Theory Appl 188: 696–723, 2021) is extended …
problems from Gebken and Peitz (J Optim Theory Appl 188: 696–723, 2021) is extended …
Descent modified conjugate gradient methods for vector optimization problems
Scalarization approaches transform vector optimization problems (VOPs) into single-
objective optimization. These approaches are quite elegant; however, they suffer from the …
objective optimization. These approaches are quite elegant; however, they suffer from the …