A5050088762- Advanced Optimization Algorithms Research
- Tensor decomposition and applications
- Matrix Theory and Algorithms
- Optimization and Variational Analysis
- Sparse and Compressive Sensing Techniques
- Face and Expression Recognition
- Contact Mechanics and Variational Inequalities
- Image and Signal Denoising Methods
- Iterative Methods for Nonlinear Equations
- Advanced Neuroimaging Techniques and Applications
- Blind Source Separation Techniques
- Elasticity and Material Modeling
- Advanced Topics in Algebra
- Medical Image Segmentation Techniques
- Model Reduction and Neural Networks
- Power System Optimization and Stability
- Topology Optimization in Engineering
- Advanced Control Systems Optimization
- Complexity and Algorithms in Graphs
- Image and Video Stabilization
- Image Retrieval and Classification Techniques
- Numerical methods in inverse problems
- Point processes and geometric inequalities
- Microwave Imaging and Scattering Analysis
- Polynomial and algebraic computation
Shenzhen University
2025
Ministry of Natural Resources
2025
Tianjin University
2015-2024
Chinese Academy of Sciences
2001-2008
University of Wisconsin–Madison
2005
Academy of Mathematics and Systems Science
2001-2004
Institute of Applied Mathematics
2003
International Space Science Institute - Beijing
1998
Nanjing University of Aeronautics and Astronautics
1994
Recently, many structured tensors are defined and their properties discussed in the literature. In this paper, we introduce a new class of tensors, called exceptionally regular (ER) tensor, which is relevant to tensor complementarity problem (TCP). We show that wide includes important as its special cases. By constructing two examples, demonstrate an ER-tensor can be, but not always, R-tensor. also within semi-positive ER-tensors coincides with R-tensors. particular, consider TCP solution...
Recently, a nonconvex relaxation of low-rank matrix recovery (LMR), called the Schatten- <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> quasi-norm minimization (0 <; 1), was introduced instead previous nuclear norm in order to approximate problem LMR closer. In this paper, we introduce notion restricted -isometry constants ≤ 1) and derive -RIP condition for exact reconstruction via minimization. particular, determine how many random,...
The linearly constrained tensor <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -rank minimization problem is an extension of matrix rank minimization. It applicable in many fields which use the multi-way data, such as data mining, machine learning and computer vision. In this paper, we adapt operator splitting technique convex relaxation to transform original into a convex, unconstrained optimization propose fixed point iterative method...
SUMMARY In this paper, we first introduce the tensor conic linear programming (TCLP), which is a generalization of space TCLP. Then an approximation method, by using sequence semidefinite problems, proposed to solve particular, reformulate extreme Z‐eigenvalue problem as special It gives numerical algorithm compute even order with dimension larger than three, improves literature. Numerical experiments show efficiency method. Copyright © 2013 John Wiley & Sons, Ltd.