D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Mathematics D-index 37 Citations 9,039 126 World Ranking 1642 National Ranking 86
Computer Science D-index 37 Citations 8,990 120 World Ranking 6640 National Ranking 642

Research.com Recognitions

Awards & Achievements

2013 - Fellow of the American Mathematical Society

2011 - SIAM Fellow For contributions to nonlinear optimization and leadership of computational mathematics in China.

Overview

What is he best known for?

The fields of study he is best known for:

  • Mathematical analysis
  • Mathematical optimization
  • Geometry

The scientist’s investigation covers issues in Mathematical optimization, Conjugate gradient method, Gradient method, Nonlinear conjugate gradient method and Algorithm. His research integrates issues of Line search, Nonlinear programming and Applied mathematics in his study of Mathematical optimization. His Applied mathematics study integrates concerns from other disciplines, such as Quasi-Newton method and Method of steepest descent.

His biological study spans a wide range of topics, including Wolfe conditions, Conjugate residual method, Mathematical analysis and Proximal Gradient Methods. In his work, Sequence, Rate of convergence, Singular problems and Quadratic growth is strongly intertwined with Numerical analysis, which is a subfield of Algorithm. His study looks at the intersection of Constrained optimization and topics like Trust region with Quadratic function, Quadratic programming and Quadratic equation.

His most cited work include:

  • A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property (686 citations)
  • Optimization Theory and Methods: Nonlinear Programming (546 citations)
  • Optimization theory and methods (456 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Mathematical optimization, Trust region, Applied mathematics, Algorithm and Nonlinear programming. His Constrained optimization, Penalty method, Gradient method, Conjugate gradient method and Quadratic programming investigations are all subjects of Mathematical optimization research. His Gradient method research includes elements of Conjugate residual method and Proximal Gradient Methods.

His Trust region research is multidisciplinary, incorporating elements of Minification, Regularization, Quadratic equation, Quadratic function and Numerical analysis. His Applied mathematics study combines topics from a wide range of disciplines, such as Quasi-Newton method, Newton's method, Quadratic growth and Convex function. His work deals with themes such as Subspace topology, Line search and Stationary point, which intersect with Algorithm.

He most often published in these fields:

  • Mathematical optimization (49.28%)
  • Trust region (23.19%)
  • Applied mathematics (22.46%)

What were the highlights of his more recent work (between 2016-2021)?

  • Mathematical optimization (49.28%)
  • Applied mathematics (22.46%)
  • Optimization problem (8.70%)

In recent papers he was focusing on the following fields of study:

Ya-xiang Yuan focuses on Mathematical optimization, Applied mathematics, Optimization problem, Augmented Lagrangian method and Orthogonality. The various areas that Ya-xiang Yuan examines in his Mathematical optimization study include Perspective and Unconstrained optimization. The Applied mathematics study combines topics in areas such as Radius, Quadratic growth, Rank, Convex function and Newton's method.

His Optimization problem research includes themes of Regularization, Quadratic equation and Stiefel manifold. The concepts of his Augmented Lagrangian method study are interwoven with issues in Bounded function, Karush–Kuhn–Tucker conditions, Combinatorics and Multiplier. His research in Orthogonality intersects with topics in Algorithm, Local convergence and Euclidean space.

Between 2016 and 2021, his most popular works were:

  • A Brief Introduction to Manifold Optimization (24 citations)
  • On Efficiently Combining Limited-Memory and Trust-Region Techniques (23 citations)
  • Adaptive Quadratically Regularized Newton Method for Riemannian Optimization (20 citations)

In his most recent research, the most cited papers focused on:

  • Mathematical analysis
  • Mathematical optimization
  • Geometry

Ya-xiang Yuan focuses on Mathematical optimization, Applied mathematics, Optimization problem, Orthogonality and Hessian matrix. In the field of Mathematical optimization, his study on Constrained optimization problem overlaps with subjects such as Control. His Applied mathematics research is multidisciplinary, incorporating perspectives in Linear differential equation, Coefficient matrix, Matrix similarity and Nonlinear programming.

His study on Optimization problem is covered under Algorithm. His Hessian matrix study overlaps with Eigendecomposition of a matrix, Line search, Matrix-free methods, Divide-and-conquer eigenvalue algorithm and Computational mathematics. His Stiefel manifold research is multidisciplinary, relying on both Tangent space, Orthogonality, Feasible region, Reduction and Coordinate descent.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property

Y. H. Dai;Y. Yuan.
Siam Journal on Optimization (1999)

1398 Citations

Optimization Theory and Methods: Nonlinear Programming

Wenyu Sun;Ya-Xiang Yuan.
(2010)

918 Citations

Optimization theory and methods

Wenyu Sun;Ya-Xiang Yuan.
(2006)

726 Citations

Global Convergence of a Cass of Quasi-Newton Methods on Convex Problems

Richard H. Byrd;Jorge Nocedal;Ya-Xiang Yuan.
SIAM Journal on Numerical Analysis (1987)

459 Citations

An Efficient Hybrid Conjugate Gradient Method for Unconstrained Optimization

Y. H. Dai;Ya-Xiang Yuan.
Annals of Operations Research (2001)

338 Citations

A trust region algorithm for equality constrained optimization

M. J. D. Powell;Y. Yuan.
Mathematical Programming (1990)

338 Citations

Combining Trust Region and Line Search Techniques

Jorge Nocedal;Ya-xiang Yuan.
(1998)

332 Citations

On the quadratic convergence of the Levenberg-Marquardt method without nonsingularity assumption

Jin-yan Fan;Ya-xiang Yuan.
Computing (2005)

280 Citations

Convergence Properties of Nonlinear Conjugate Gradient Methods

Yuhong Dai;Jiye Han;Guanghui Liu;Defeng Sun.
Siam Journal on Optimization (1999)

277 Citations

A modified BFGS algorithm for unconstrained optimization

Ya-Xiang Yuan.
Ima Journal of Numerical Analysis (1991)

243 Citations

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