Gonglin Yuan
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View article: Does the digital transformation of enterprises affect capital mismatch? evidence from Chinese listed firms
Does the digital transformation of enterprises affect capital mismatch? evidence from Chinese listed firms Open
In this study, based on the data of the Chinese listed firms, the effect of digital transformation on capital mismatch was examined. And the potential mechanism was also further discussed. It was found that digital transformation can signi…
View article: An inertial conjugate gradient projection method for large-scale nonlinear equations and its application in the image restoration problems
An inertial conjugate gradient projection method for large-scale nonlinear equations and its application in the image restoration problems Open
Based on the acceleration effect of the inertial extrapolation technique on the convergence of iterative sequences, the number of algorithms incorporating this technique has gradually increased in recent years. Currently, there is a relati…
View article: A Stochastic Inertial Limited Memory BFGS Algorithm Based on the Mathematical Model of Rural Pipeline Network and its Application in Machine Learning
A Stochastic Inertial Limited Memory BFGS Algorithm Based on the Mathematical Model of Rural Pipeline Network and its Application in Machine Learning Open
Stochastic algorithms are critical in addressing complex rural pipe networks and non-convex stochastic optimization problems. With the development of artificial intelligence, large-scale optimization problems that cannot be solved effectiv…
View article: An Inertial-relaxed Conjugate Gradient Algorithm Based on the Mathematical Model of Rural Pipeline Network in Image-restoration
An Inertial-relaxed Conjugate Gradient Algorithm Based on the Mathematical Model of Rural Pipeline Network in Image-restoration Open
As time progresses, the complexity of nonlinear systems continues to escalate, presenting significant challenges in problem-solving, particularly with increasing dimensions. To address this challenge, based on solving nonlinear equation sy…
View article: A Modified Wei-Yao-Liu Stochastic Conjugate Gradient Algorithm in Machine Learning
A Modified Wei-Yao-Liu Stochastic Conjugate Gradient Algorithm in Machine Learning Open
The Wei-Yao-Liu (WYL) Conjugate Gradient (CG) algorithm exhibits favourable attributes, notably sufficient descent and trust domain characteristics, in the context of solving unconstrained optimization problems. The exploration and enhance…
View article: A Modified Three-terms CG Method with the MWWP Line Search and its Application to Image Restoration
A Modified Three-terms CG Method with the MWWP Line Search and its Application to Image Restoration Open
In this article, an innovative method, which integrates the optimized three-term Polak–Ribière–Polyak (PRP) conjugate gradient (CG) method with the improved WWP line search rule, is presented and applied in the field of image restoration. …
View article: A Diagonal BFGS Update Algorithm with Inertia Acceleration Technology for Minimizations
A Diagonal BFGS Update Algorithm with Inertia Acceleration Technology for Minimizations Open
We integrate the diagonal quasi-Newton update approach with the enhanced BFGS formula proposed by Wei, Z., Yu, G., Yuan, G., Lian, Z. \cite{b1}, incorporating extrapolation techniques and inertia acceleration technology. This method, desig…
View article: A Stochastic Recursive Gradient Algorithm with Inertial Extrapolation for Non-convex Problems and machine learning
A Stochastic Recursive Gradient Algorithm with Inertial Extrapolation for Non-convex Problems and machine learning Open
In recent years, extrapolation acceleration technology has been widely employed across various algorithms; however, its application in machine learning has yielded limited achievements. Therefore, building upon the foundation of inertial a…
View article: A Feasible Solution for Image Encryption, Restoration, and Colorization Based on the Methods of Weighted Conjugate Gradient, RSA Encryption, and Deep Learning.
A Feasible Solution for Image Encryption, Restoration, and Colorization Based on the Methods of Weighted Conjugate Gradient, RSA Encryption, and Deep Learning. Open
In contemporary society, people are increasingly aware of environmental issues. Coinciding with this development is the arrival of the era of big data, accompanied by significant advances in artificial intelligence. These technologies are …
View article: Stochastic three-term conjugate gradient method with variance technique for non-convex learning
Stochastic three-term conjugate gradient method with variance technique for non-convex learning Open
In the training process of machine learning, the minimization of the empirical risk loss function is often used to measure the difference between the model’s predicted value and the real value. Stochastic gradient descent is very popular f…
View article: An efficient modified HS conjugate gradient algorithm in machine learning
An efficient modified HS conjugate gradient algorithm in machine learning Open
The Hestenes-Stiefe (HS) conjugate gradient method is very effective in resolving larger-scale sophisticated smoothing optimization tasks due to its low computational requirements and high computational efficiency. Additionally, the algori…
View article: A Conjugate Gradient Algorithm Based on Cable-Stayed Bridges Model for Nonconvex Minimization
A Conjugate Gradient Algorithm Based on Cable-Stayed Bridges Model for Nonconvex Minimization Open
This paper examines the optimization problem of cable force in the construction phase of single-tower cable-stayed bridges, transforming it into an unconstrained problem that aims to control the vertical displacement of main beam nodes and…
View article: Conjugate Gradient-Style Momentum Method with Adaptive Difference for Image Classification
Conjugate Gradient-Style Momentum Method with Adaptive Difference for Image Classification Open
SGDM is a classic momentum method, which is widely used in machine learning due to its high convergence accuracy. One disadvantage of SGDM is the slow convergence speed. In recent years, some scholars have proposed SGDMD, which attempts to…
View article: A Stochastic Algorithm Based on Cable-stayed Bridges Model for Stochastic Optimization in Machine Learning
A Stochastic Algorithm Based on Cable-stayed Bridges Model for Stochastic Optimization in Machine Learning Open
This paper takes the economic cost of road design as the objective function to study the optimization of road construction cost. It is well-know that stochastic optimization is widely regarded as one of the most important and difficult pro…
View article: An accelerated descent CG algorithm with clustering the eigenvalues for large-scale nonconvex unconstrained optimization and its application in image restoration problems
An accelerated descent CG algorithm with clustering the eigenvalues for large-scale nonconvex unconstrained optimization and its application in image restoration problems Open
Conjugate gradient methods are widely used for solving large-scale unconstrained optimization problems. Since they have the attractive practical factors of simple computation and low memory requirement, interesting theoretical features of …
View article: Cross-Regional Allocation of Human Capital and Sustainable Development of China’s Regional Economy—Based on the Perspective of Population Mobility
Cross-Regional Allocation of Human Capital and Sustainable Development of China’s Regional Economy—Based on the Perspective of Population Mobility Open
Using the panel data of cities at the prefecture level and above in China, based on the endogenous growth theory, this study aims to explore the influence of the cross-regional allocation of human capital on regional economic sustainable d…
View article: Nonmonotone conjugate gradient algorithm without gradient Lipschitz continuity for nonconvex minimizations
Nonmonotone conjugate gradient algorithm without gradient Lipschitz continuity for nonconvex minimizations Open
For this article, a nonmonotone nonlinear gradient method framework is designed for nonconvex optimization problems. Using two classical nonmonotone line search techniques, the global convergence is proven, and we do not need the assumptio…
View article: Globally convergent conjugate gradient algorithms without the Lipschitz condition for nonconvex optimization
Globally convergent conjugate gradient algorithms without the Lipschitz condition for nonconvex optimization Open
It is well known that under the Wolfe–Powell inexact line search, the global convergence of the nonlinear conjugate gradient method always requires the Lipschitz continuous condition for nonconvex functions. In this paper, we find that the…
View article: Infeasibility of constructing a special orthogonal matrix for the deterministic remote preparation of arbitrary n-qubit state
Infeasibility of constructing a special orthogonal matrix for the deterministic remote preparation of arbitrary n-qubit state Open
In this paper, we present a polynomial-complexity algorithm to construct a special orthogonal matrix for the deterministic remote state preparation (DRSP) of an arbitrary $n$-qubit state, and prove that if $n > 3$, such matrices do not exi…
View article: Global convergence of a modified Broyden family method for nonconvex functions
Global convergence of a modified Broyden family method for nonconvex functions Open
The Broyden family method is one of the most effective methods for solving unconstrained optimization problems. However, the study of the global convergence of the Broyden family method is not sufficient. In this paper, a new Broyden famil…
View article: Stochastic Bigger Subspace Algorithms for Nonconvex Stochastic Optimization
Stochastic Bigger Subspace Algorithms for Nonconvex Stochastic Optimization Open
It is well known that the stochastic optimization problem can be regarded as one of the most hard problems since, in most of the cases, the values of and its gradient are often not easily to be solved, or the is normally not given clearl…
View article: A Modified Three-Term Conjugate Gradient Algorithm for Large-Scale Nonsmooth Convex Optimization
A Modified Three-Term Conjugate Gradient Algorithm for Large-Scale Nonsmooth Convex Optimization Open
It is well known that Newton and quasi-Newton algorithms are effective to small and medium scale smooth problems because they take full use of corresponding gradient function’s information but fail to solve nonsmooth pr... | Find, read and…
View article: A conjugate gradient algorithm and its application in large-scale optimization problems and image restoration
A conjugate gradient algorithm and its application in large-scale optimization problems and image restoration Open
To solve large-scale unconstrained optimization problems, a modified PRP conjugate gradient algorithm is proposed and is found to be interesting because it combines the steepest descent algorithm with the conjugate gradient method and succ…
View article: A Three-Term Conjugate Gradient Algorithm with Quadratic Convergence for Unconstrained Optimization Problems
A Three-Term Conjugate Gradient Algorithm with Quadratic Convergence for Unconstrained Optimization Problems Open
This paper further studies the WYL conjugate gradient (CG) formula with and presents a three-term WYL CG algorithm, which has the sufficiently descent property without any conditions. The global convergence and the linear convergence are …
View article: An active-set algorithm for solving large-scale nonsmooth optimization models with box constraints
An active-set algorithm for solving large-scale nonsmooth optimization models with box constraints Open
It is well known that the active set algorithm is very effective for smooth box constrained optimization. Many achievements have been obtained in this field. We extend the active set method to nonsmooth box constrained optimization problem…