Lek-Heng Lim

Q: What Schools Are Affiliated With Lek-Heng Lim

Lek-Heng Lim is affiliated with the following schools: University of California, Berkeley, University of Chicago, Stanford University

Lek-Heng Lim's AcademicInfluence.com Rankings

Lek-Heng Lim

Mathematics

#4343

World Rank

#6190

Historical Rank

Combinatorics

#101

World Rank

#109

Historical Rank

Algebraic Geometry

#189

World Rank

#197

Historical Rank

Measure Theory

#3090

World Rank

#3662

Historical Rank

mathematics Degrees

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Mathematics

Why Is Lek-Heng Lim Influential?

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According to Wikipedia, Lek-Heng Lim is a Singaporean mathematician. Lim earned a bachelor's degree at the National University of Singapore, studied for his master's at Cornell University and the University of Cambridge, and completed a doctorate at Stanford University. Lim started his teaching career at the University of California, Berkeley, where he served as Charles Morrey Assistant Professor. He later joined the University of Chicago faculty. While affiliated with Chicago, Lim won the James H. Wilkinson Prize in Numerical Analysis and Scientific Computing and in 2017, followed by the 2020 Hans Schneider Prize in Linear Algebra. In 2020, Lim was also elected a fellow of the American Mathematical Society.

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Lek-Heng Lim's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Most Tensor Problems Are NP-Hard (2009) (1022)
Singular values and eigenvalues of tensors: a variational approach (2005) (954)
Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem (2006) (886)
Symmetric Tensors and Symmetric Tensor Rank (2008) (568)
Statistical ranking and combinatorial Hodge theory (2008) (322)
Nonnegative approximations of nonnegative tensors (2009) (184)
Rank aggregation via nuclear norm minimization (2011) (179)
Nuclear norm of higher-order tensors (2014) (164)
Quasi-Newton Methods on Grassmannians and Multilinear Approximations of Tensors (2009) (130)
Schubert Varieties and Distances between Subspaces of Different Dimensions (2014) (121)
Multiarray Signal Processing: Tensor decomposition meets compressed sensing (2010) (111)
Hodge Laplacians on graphs (2015) (109)
Multilinear PageRank (2014) (104)
Tensors and Hypermatrices (2013) (102)
Blind Multilinear Identification (2012) (95)
Shift-invariant multilinear decomposition of neuroimaging data (2008) (93)
Tropical Geometry of Deep Neural Networks (2018) (89)
The Spacey Random Walk: A Stochastic Process for Higher-Order Data (2016) (82)
Every Matrix is a Product of Toeplitz Matrices (2013) (71)
Topology of deep neural networks (2020) (64)
Uniqueness of Nonnegative Tensor Approximations (2014) (48)
Semialgebraic Geometry of Nonnegative Tensor Rank (2015) (40)
Future Directions in Tensor-Based Computation and Modeling (2009) (39)
Tensor network ranks (2018) (37)
ParNes: a rapidly convergent algorithm for accurate recovery of sparse and approximately sparse signals (2009) (33)
A Direct Integral Decomposition of the Wavelet Representation (2000) (30)
Distance between subspaces of different dimensions (2014) (29)
Fast Randomized Iteration: Diffusion Monte Carlo through the Lens of Numerical Linear Algebra (2015) (27)
Higher-Order Tensors in Diffusion Imaging (2014) (26)
Best multilinear rank approximation of tensors with quasi-Newton methods on Grassmannians (2008) (25)
Foundations of numerical multilinear algebra: decomposition and approximation of tensors (2007) (25)
The Computational Complexity of Duality (2016) (23)
Computational Complexity of Tensor Nuclear Norm (2014) (23)
Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top (2015) (22)
Fast Structured Matrix Computations: Tensor Rank and Cohn–Umans Method (2016) (20)
Optimal solutions to non-negative PARAFAC/multilinear NMF always exist (2005) (20)
Optimization on flag manifolds (2019) (19)
Genericity And Rank Deficiency Of High Order Symmetric Tensors (2006) (19)
Tensors in computations (2021) (17)
Algorithmic and statistical challenges in modern largescale data analysis are the focus of MMDS 2008 (2008) (17)
Theoretical and computational aspects of entanglement (2017) (17)
Distances Between Probability Distributions of Different Dimensions (2020) (17)
PRINCIPAL CUMULANT COMPONENT ANALYSIS (2009) (15)
Versatile, robust, and efficient tractography with constrained higher-order tensor fODFs (2017) (14)
The spectral theory of tensors and its applications (2013) (13)
Recht-Ré Noncommutative Arithmetic-Geometric Mean Conjecture is False (2020) (12)
Learning Subspaces of Different Dimension (2014) (12)
The Grassmannian of affine subspaces (2018) (11)
Numerical Algorithms on the Affine Grassmannian (2016) (10)
Complex best r-term approximations almost always exist in finite dimensions (2017) (10)
Learning to rank with combinatorial Hodge theory (2008) (10)
Geometric Distance Between Positive Definite Matrices of Different Dimensions (2018) (10)
An elementary and unified proof of Grothendieck’s inequality (2017) (9)
Cohomology of Cryo-Electron Microscopy (2016) (9)
Topology of tensor ranks (2018) (8)
Semi-Riemannian Manifold Optimization (2018) (7)
Sparse Representations and Low-Rank Tensor Approximation (2011) (7)
Learning Subspaces of Different Dimensions (2021) (7)
Ubiquity of the exponent of matrix multiplication (2020) (7)
Algorithms for structured matrix-vector product of optimal bilinear complexity (2016) (7)
Grothendieck constant is norm of Strassen matrix multiplication tensor (2017) (5)
What is an equivariant neural network? (2022) (5)
Simpler Grassmannian optimization (2020) (5)
Macroeconomics and FinTech: Uncovering Latent Macroeconomic Effects on Peer-to-Peer Lending (2017) (5)
Fiber product homotopy method for multiparameter eigenvalue problems (2018) (4)
Complex tensors almost always have best low-rank approximations (2017) (4)
Statistical Estimation and the Affine Grassmannian (2016) (3)
Self-concordance is NP-hard (2013) (3)
Fast and Accurate Multi-tissue Deconvolution Using SHORE and H-psd Tensors (2016) (3)
Workshop on Algorithms for Modern Massive Datasets (2006) (2)
Symmetric Grothendieck inequality (2020) (2)
Higher-Order Cone Programming (2018) (2)
MMDS 2008 : Algorithmic and Statistical Challenges in Modern Large-Scale Data Analysis are the Focus (2009) (2)
Bridging the Gap Between Numerical Linear Algebra , Theoretical Computer Science , and Data Applications By Gene (2006) (1)
Security of the Cao-Li Public Key Cryptosystem (2000) (1)
Multilinear algebra in machine learning and signal processing (2007) (1)
Inverting a complex matrix (2022) (1)
Best k-Layer Neural Network Approximations (2019) (1)
Numerical stability and tensor nuclear norm (2022) (1)
MMDS 2008 : Algorithmic and Statistical Challenges in Modern Large-Scale Data Analysis , Part I (2009) (1)
Every Matrix is a Product of Toeplitz Matrices (2015) (0)
Simpler flag optimization (2022) (0)
LU decomposition and Toeplitz decomposition of a neural network (2022) (0)
Optimization on flag manifolds (2021) (0)
NUMERICAL ALGORITHMS ON THE AFFINE GRASSMANNIAN \ast (2019) (0)
Preface to the Special Issue on Statistics (2015) (0)
Self-concordance is NP-hard (2016) (0)
Acknowledgement I Am Grateful to Evira (0)
Title: Graph Gauge Theory and Vector Diffusion Maps Title: a Random Walk on Image Patches Title: Dimension Reduction, Coarse-graining and Data Assimilation in High-dimensional Dynamical Systems Title: Geometry and Topology in Dimension Reduction Title: Optimal Phase Transitions in Compressed Sensing (0)
Quasi-Newton algorithm for best multi-linear rank approximation of tensors (2007) (0)
Accurate Solutions of Polynomial Eigenvalue Problems (2017) (0)
Stochastic Steffensen method (2022) (0)
ParNes: a rapidly convergent algorithm for accurate recovery of sparse and approximately sparse signals (2012) (0)
Special Issue: Polynomial and Tensor Optimization (2022) (0)
Preface (2016) (0)
Haagerup bound for quaternionic Grothendieck inequality (2022) (0)
C Xxxx Society for Industrial and Applied Mathematics the Computational Complexity of Duality * 3 (0)
Best k-Layer Neural Network Approximations (2021) (0)
On the Semialgebraic Structure of the Set of Nonnegative Tensors (2015) (0)
New Results - Algebraic Geometric Analysis (2008) (0)
SEMIALGEBRAIC GEOMETRY OF NONNEGATIVE (2016) (0)
The Grassmannian of affine subspaces (2020) (0)
Generalized matrix nearness problems (2022) (0)
“ The Field is as Exciting as Ever ” (2017) (0)
Combinatorial Hodge Theory and A Geometric Approach to Ranking (2008) (0)
Rank-constrained Hyperbolic Programming (2022) (0)
Preface to the special issue on tensors and multilinear algebra (2013) (0)
Fast Structured Matrix Computations: Tensor Rank and Cohn–Umans Method (2016) (0)
Multilinear Computing and Multilinear Algebraic Geometry (2016) (0)
Path-connectedness of tensor ranks (2019) (0)

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Other Resources About Lek-Heng Lim

What Schools Are Affiliated With Lek-Heng Lim?

Lek-Heng Lim is affiliated with the following schools:

Lek-Heng Lim's Academic­Influence.com Rankings

Why Is Lek-Heng Lim Influential?

Lek-Heng Lim's Published Works

Published Works

Other Resources About Lek-Heng Lim

What Schools Are Affiliated With Lek-Heng Lim?

Lek-Heng Lim's AcademicInfluence.com Rankings