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 32 Citations 3,884 125 World Ranking 2438 National Ranking 60

Overview

What is he best known for?

The fields of study he is best known for:

  • Mathematical analysis
  • Algebra
  • Mathematical optimization

His primary areas of investigation include Applied mathematics, Mathematical optimization, Optimization problem, Nonlinear conjugate gradient method and Conjugate gradient method. His Applied mathematics research incorporates themes from Function, Polynomial, Mathematical analysis and Hessian matrix. His Mathematical optimization study combines topics in areas such as Numerical analysis and Nonlinear programming.

His work carried out in the field of Optimization problem brings together such families of science as Minimax, Bounded function, Sequence, Local convergence and Stationary point. Guoyin Li works mostly in the field of Nonlinear conjugate gradient method, limiting it down to topics relating to Line search and, in certain cases, Trust region, Gradient method, Proximal Gradient Methods and Algorithm, as a part of the same area of interest. His Linear programming research integrates issues from Matrix, Uncertain data, Robust optimization and Affine transformation.

His most cited work include:

  • Global Convergence of Splitting Methods for Nonconvex Composite Optimization (216 citations)
  • New quasi-Newton methods for unconstrained optimization problems (121 citations)
  • New conjugacy condition and related new conjugate gradient methods for unconstrained optimization (104 citations)

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

The scientist’s investigation covers issues in Mathematical optimization, Applied mathematics, Robust optimization, Linear programming and Discrete mathematics. Within one scientific family, Guoyin Li focuses on topics pertaining to Convex analysis under Mathematical optimization, and may sometimes address concerns connected to Subderivative and Combinatorics. Guoyin Li has researched Applied mathematics in several fields, including Quadratic equation, Quadratic programming, Sequence, Bounded function and Optimization problem.

The Robust optimization study which covers Conic section that intersects with Support vector machine. His studies deal with areas such as Separable space, Feasible region and Linear inequality as well as Linear programming. Guoyin Li combines subjects such as Rate of convergence, Real algebraic geometry, Algebraic number and Dykstra's projection algorithm with his study of Discrete mathematics.

He most often published in these fields:

  • Mathematical optimization (41.91%)
  • Applied mathematics (31.62%)
  • Robust optimization (16.18%)

What were the highlights of his more recent work (between 2017-2021)?

  • Mathematical optimization (41.91%)
  • Robust optimization (16.18%)
  • Applied mathematics (31.62%)

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

Guoyin Li spends much of his time researching Mathematical optimization, Robust optimization, Applied mathematics, Conic section and Quadratic equation. The various areas that Guoyin Li examines in his Mathematical optimization study include Ball, Projection and Convex optimization. His Robust optimization research is multidisciplinary, incorporating elements of Duality, Range, Data classification, Artificial intelligence and Feature data.

His Applied mathematics research includes elements of Rate of convergence, Polynomial optimization and Dual. His Conic section study also includes

  • Feasible region, Affine transformation, Parameterized complexity, Realization and Decision rule most often made with reference to Linear programming,
  • Support vector machine which intersects with area such as Surrogate model and Numerical analysis. Relaxation, Second-order cone programming, Separable space, Cone programming and Class is closely connected to Quadratic programming in his research, which is encompassed under the umbrella topic of Quadratic equation.

Between 2017 and 2021, his most popular works were:

  • Calculus of the Exponent of Kurdyka–Łojasiewicz Inequality and Its Applications to Linear Convergence of First-Order Methods (98 citations)
  • Nonlinear behaviour and stability of functionally graded porous arches with graphene platelets reinforcements (64 citations)
  • A semidefinite program approach for computing the maximum eigenvalue of a class of structured tensors and its applications in hypergraphs and copositivity test (33 citations)

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

  • Mathematical analysis
  • Algebra
  • Mathematical optimization

Guoyin Li mostly deals with Rate of convergence, Polynomial, Applied mathematics, Mathematical optimization and Convex optimization. The study incorporates disciplines such as Dimension, Tuple, Sublinear function and Rank in addition to Rate of convergence. His biological study spans a wide range of topics, including Time complexity, Symmetric tensor, Eigenvalues and eigenvectors and Invariants of tensors.

His Applied mathematics study incorporates themes from Polynomial matrix, Matrix polynomial, Dykstra's projection algorithm, Solution set and Sequence. His Mathematical optimization study frequently involves adjacent topics like Probabilistic logic. His studies in Convex optimization integrate themes in fields like Multi-objective optimization, Quadratic equation, Ball and Robust optimization.

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

Global Convergence of Splitting Methods for Nonconvex Composite Optimization

Guoyin Li;Ting Kei Pong.
Siam Journal on Optimization (2015)

296 Citations

Global Convergence of Splitting Methods for Nonconvex Composite Optimization

Guoyin Li;Ting Kei Pong.
Siam Journal on Optimization (2015)

296 Citations

New quasi-Newton methods for unconstrained optimization problems

Zengxin Wei;Guoyin Li;Liqun Qi.
Applied Mathematics and Computation (2006)

251 Citations

New quasi-Newton methods for unconstrained optimization problems

Zengxin Wei;Guoyin Li;Liqun Qi.
Applied Mathematics and Computation (2006)

251 Citations

Calculus of the Exponent of Kurdyka–Łojasiewicz Inequality and Its Applications to Linear Convergence of First-Order Methods

Guoyin Li;Ting Kei Pong.
Foundations of Computational Mathematics (2018)

180 Citations

Calculus of the Exponent of Kurdyka–Łojasiewicz Inequality and Its Applications to Linear Convergence of First-Order Methods

Guoyin Li;Ting Kei Pong.
Foundations of Computational Mathematics (2018)

180 Citations

New conjugacy condition and related new conjugate gradient methods for unconstrained optimization

Guoyin Li;Chunming Tang;Zengxin Wei.
Journal of Computational and Applied Mathematics (2007)

162 Citations

New conjugacy condition and related new conjugate gradient methods for unconstrained optimization

Guoyin Li;Chunming Tang;Zengxin Wei.
Journal of Computational and Applied Mathematics (2007)

162 Citations

Strong Duality in Robust Convex Programming: Complete Characterizations

V. Jeyakumar;G. Y. Li.
Siam Journal on Optimization (2010)

129 Citations

Strong Duality in Robust Convex Programming: Complete Characterizations

V. Jeyakumar;G. Y. Li.
Siam Journal on Optimization (2010)

129 Citations

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