具有渐近消失阻尼的惯性多目标梯度样系统的快速收敛性

IF 2.6 1区 数学 Q1 MATHEMATICS, APPLIED SIAM Journal on Optimization Pub Date : 2024-07-02 DOI:10.1137/23m1588512
Konstantin Sonntag, Sebastian Peitz
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引用次数: 0

摘要

SIAM 优化期刊》,第 34 卷第 3 期,第 2259-2286 页,2024 年 9 月。 摘要我们提出了一个与无约束凸平滑多目标优化相关的新的类梯度动力系统,它涉及惯性效应和渐近消失阻尼。据我们所知,该系统是第一个包含渐近消失阻尼的多目标优化问题的惯性类梯度系统,拓展了以前在 [H. Attouch and G. Garrigou] 中提出的观点。Attouch 和 G. Garrigos,多目标优化:An Inertial Dynamical Approach to Pareto Optima, preprint, arXiv:1506.02823, 2015]中的观点。我们证明了该系统在有限维度上的解的存在性,并进一步证明了其有界解弱收敛于弱帕累托最优点。此外,我们还获得了用绩优函数测量的函数值的[math]阶收敛率。这种方法为开发多目标优化的快速梯度方法奠定了良好基础。
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Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping
SIAM Journal on Optimization, Volume 34, Issue 3, Page 2259-2286, September 2024.
Abstract. We present a new gradient-like dynamical system related to unconstrained convex smooth multiobjective optimization which involves inertial effects and asymptotic vanishing damping. To the best of our knowledge, this system is the first inertial gradient-like system for multiobjective optimization problems including asymptotic vanishing damping, expanding the ideas previously laid out in [H. Attouch and G. Garrigos, Multiobjective Optimization: An Inertial Dynamical Approach to Pareto Optima, preprint, arXiv:1506.02823, 2015]. We prove existence of solutions to this system in finite dimensions and further prove that its bounded solutions converge weakly to weakly Pareto optimal points. In addition, we obtain a convergence rate of order [math] for the function values measured with a merit function. This approach presents a good basis for the development of fast gradient methods for multiobjective optimization.
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来源期刊
SIAM Journal on Optimization
SIAM Journal on Optimization 数学-应用数学
CiteScore
5.30
自引率
9.70%
发文量
101
审稿时长
6-12 weeks
期刊介绍: The SIAM Journal on Optimization contains research articles on the theory and practice of optimization. The areas addressed include linear and quadratic programming, convex programming, nonlinear programming, complementarity problems, stochastic optimization, combinatorial optimization, integer programming, and convex, nonsmooth and variational analysis. Contributions may emphasize optimization theory, algorithms, software, computational practice, applications, or the links between these subjects.
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