一类基于惯性的无导数投影法及其应用

IF 1.8 3区 数学 Q1 MATHEMATICS Numerical Linear Algebra with Applications Pub Date : 2023-09-22 DOI:10.1002/nla.2533
Pengjie Liu, Hu Shao, Zihang Yuan, Jianhao Zhou
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引用次数: 0

摘要

由于约束非线性方程组在各种工程应用中的广泛应用,人们一直在努力开发求解约束非线性方程组的有效方法。在本文中,我们提出了一组基于惯性的无导数投影方法,其中有一个校正步骤,可以灵活地选择无导数搜索方向。该族不需要在每次迭代时计算相应的雅可比矩阵或近似矩阵,具有以下理论性质:(1)基于惯性的修正方向框架总是自动满足充分下降和信赖域性质,而不需要特定的搜索方向,并且独立于任何直线搜索;(ii)在映射上较弱的单调性条件下证明了所提族的全局收敛性,没有典型的单调性或伪单调性假设;(3)在稍强的假设条件下,建立了拟合家族收敛速度的结果。此外,我们提出了两种有效的基于惯性的无导数投影方法,每种方法都将特定的搜索方向嵌入到所提出的族中。我们对某些测试问题进行了初步的数值实验,并与现有方法进行了比较,证明了所提出方法的有效性和优越性。此外,我们利用这些方法来解决压缩感知应用中的稀疏信号恢复和图像恢复。
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A family of inertial‐based derivative‐free projection methods with a correction step for constrained nonlinear equations and their applications
Abstract Numerous attempts have been made to develop efficient methods for solving the system of constrained nonlinear equations due to its widespread use in diverse engineering applications. In this article, we present a family of inertial‐based derivative‐free projection methods with a correction step for solving such system, in which the selection of the derivative‐free search direction is flexible. This family does not require the computation of corresponding Jacobian matrix or approximate matrix at every iteration and possess the following theoretical properties: (i) the inertial‐based corrected direction framework always automatically satisfies the sufficient descent and trust region properties without specific search directions, and is independent of any line search; (ii) the global convergence of the proposed family is proven under a weaker monotonicity condition on the mapping , without the typical monotonicity or pseudo‐monotonicity assumption; (iii) the results about convergence rate of the proposed family are established under slightly stronger assumptions. Furthermore, we propose two effective inertial‐based derivative‐free projection methods, each embedding a specific search direction into the proposed family. We present preliminary numerical experiments on certain test problems to demonstrate the effectiveness and superiority of the proposed methods in comparison with existing ones. Additionally, we utilize these methods for solving sparse signal restorations and image restorations in compressive sensing applications.
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来源期刊
CiteScore
3.40
自引率
2.30%
发文量
50
审稿时长
12 months
期刊介绍: Manuscripts submitted to Numerical Linear Algebra with Applications should include large-scale broad-interest applications in which challenging computational results are integral to the approach investigated and analysed. Manuscripts that, in the Editor’s view, do not satisfy these conditions will not be accepted for review. Numerical Linear Algebra with Applications receives submissions in areas that address developing, analysing and applying linear algebra algorithms for solving problems arising in multilinear (tensor) algebra, in statistics, such as Markov Chains, as well as in deterministic and stochastic modelling of large-scale networks, algorithm development, performance analysis or related computational aspects. Topics covered include: Standard and Generalized Conjugate Gradients, Multigrid and Other Iterative Methods; Preconditioning Methods; Direct Solution Methods; Numerical Methods for Eigenproblems; Newton-like Methods for Nonlinear Equations; Parallel and Vectorizable Algorithms in Numerical Linear Algebra; Application of Methods of Numerical Linear Algebra in Science, Engineering and Economics.
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