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- Liqun Qi

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Mathematics
H-index
73
Citations
20,367
384
World Ranking
98
National Ranking
3

- Mathematical analysis
- Algebra
- Eigenvalues and eigenvectors

Liqun Qi spends much of his time researching Tensor, Mathematical optimization, Combinatorics, Eigenvalues and eigenvectors and Mathematical analysis. The study incorporates disciplines such as Superlinear convergence, Quadratic equation, Mixed complementarity problem and Complementarity theory in addition to Mathematical optimization. His study in Combinatorics is interdisciplinary in nature, drawing from both Discrete mathematics, Spectral radius and Nonnegative matrix.

His work deals with themes such as Positive definiteness, Invertible matrix, Diagonal and Homogeneous polynomial, which intersect with Eigenvalues and eigenvectors. His biological study spans a wide range of topics, including Geometry and Newton's method, Nonlinear system. His work carried out in the field of Newton's method brings together such families of science as Smoothing, Hessian matrix, Applied mathematics, Numerical analysis and Local convergence.

- A nonsmooth version of Newton's method (1190 citations)
- Eigenvalues of a real supersymmetric tensor (997 citations)
- Convergence Analysis of Some Algorithms for Solving Nonsmooth Equations (621 citations)

His primary areas of study are Tensor, Pure mathematics, Mathematical optimization, Combinatorics and Mathematical analysis. His Tensor study incorporates themes from Positive-definite matrix, Eigenvalues and eigenvectors and Applied mathematics. The Invertible matrix research he does as part of his general Pure mathematics study is frequently linked to other disciplines of science, such as Third order, therefore creating a link between diverse domains of science.

His Mathematical optimization study incorporates themes from Smoothing and Rate of convergence. His Combinatorics research is multidisciplinary, incorporating elements of Discrete mathematics, Upper and lower bounds and Spectral radius. He studied Mathematical analysis and Newton's method that intersect with Local convergence and Numerical analysis.

- Tensor (39.14%)
- Pure mathematics (23.60%)
- Mathematical optimization (22.47%)

- Tensor (39.14%)
- Pure mathematics (23.60%)
- Combinatorics (18.54%)

His primary scientific interests are in Tensor, Pure mathematics, Combinatorics, Eigenvalues and eigenvectors and Symmetric tensor. His Tensor research includes themes of Matrix, Rank, Applied mathematics and Complementarity theory. His Pure mathematics research includes elements of Singular value, Positive-definite matrix and Matrix norm.

His Eigenvalues and eigenvectors study combines topics in areas such as Simple and Mathematical physics. His Symmetric tensor study contributes to a more complete understanding of Mathematical analysis. His Tensor density research incorporates elements of Tensor product of Hilbert spaces and Weyl tensor.

- Tensor Analysis: Spectral Theory and Special Tensors (186 citations)
- Tensor Complementarity Problem and Semi-positive Tensors (102 citations)
- Formulating an n-person noncooperative game as a tensor complementarity problem (87 citations)

- Mathematical analysis
- Algebra
- Eigenvalues and eigenvectors

The scientist’s investigation covers issues in Tensor, Eigenvalues and eigenvectors, Complementarity theory, Symmetric tensor and Combinatorics. His Tensor research is multidisciplinary, relying on both Upper and lower bounds, Mathematical analysis, Pure mathematics, Applied mathematics and Homogeneous polynomial. The concepts of his Mathematical analysis study are interwoven with issues in Positive definiteness and Explained sum of squares.

His studies deal with areas such as Iterated function and Sequence as well as Eigenvalues and eigenvectors. His Symmetric tensor study combines topics from a wide range of disciplines, such as Tensor contraction, Stability, Semidefinite programming, Invariants of tensors and Tensor field. As a part of the same scientific family, Liqun Qi mostly works in the field of Tensor contraction, focusing on Tensor density and, on occasion, Tensor product of Hilbert spaces.

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.

A nonsmooth version of Newton's method

Liqun Qi;Jie Sun.

Mathematical Programming **(1993)**

1744 Citations

Eigenvalues of a real supersymmetric tensor

Liqun Qi.

Journal of Symbolic Computation **(2005)**

1091 Citations

Convergence Analysis of Some Algorithms for Solving Nonsmooth Equations

Liqun Qi.

Mathematics of Operations Research **(1993)**

953 Citations

Nonsmooth Equations: Motivation and Algorithms

Jong-Shi Pang;Liqun Qi.

Siam Journal on Optimization **(1993)**

470 Citations

A new look at smoothing Newton methods for nonlinear complementarity problems and box constrained variational inequalities

Liqun Qi;Defeng Sun;Guanglu Zhou.

Mathematical Programming **(2000)**

443 Citations

A smoothing method for mathematical programs with equilibrium constraints

Francisco Facchinei;Houyuan Jiang;Liqun Qi.

Mathematical Programming **(1999)**

397 Citations

Global and superlinear convergence of the smoothing Newton method and its application to general box constrained variational inequalities

X. Chen;L. Qi;D. Sun.

Mathematics of Computation **(1998)**

346 Citations

$M$-Tensors and Some Applications

Liping Zhang;Liqun Qi;Guanglu Zhou.

SIAM Journal on Matrix Analysis and Applications **(2014)**

286 Citations

Does diffusion kurtosis imaging lead to better neural tissue characterization? A rodent brain maturation study

Matthew M. Cheung;Edward S. Hui;Kevin C. Chan;Joseph A. Helpern.

NeuroImage **(2009)**

282 Citations

Finding the Largest Eigenvalue of a Nonnegative Tensor

Michael Ng;Liqun Qi;Guanglu Zhou.

SIAM Journal on Matrix Analysis and Applications **(2009)**

282 Citations

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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