Yun-Bin Zhao
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View article: Splitting Alternating Algorithms for Sparse Solutions of Linear Systems with Concatenated Orthogonal Matrices
Splitting Alternating Algorithms for Sparse Solutions of Linear Systems with Concatenated Orthogonal Matrices Open
A class of splitting alternating algorithms is proposed for finding the sparse solution of linear systems with concatenated orthogonal matrices. Depending on the number of matrices concatenated, the proposed algorithms are classified into …
View article: Dynamic Thresholding Algorithm with Memory for Linear Inverse Problems
Dynamic Thresholding Algorithm with Memory for Linear Inverse Problems Open
The relaxed optimal $k$-thresholding pursuit (ROTP) is a recent algorithm for linear inverse problems. This algorithm is based on the optimal $k$-thresholding technique which performs vector thresholding and error metric reduction simultan…
View article: On NP-Hardness of $L_1/L_2$ Minimization and Bound Theory of Nonzero Entries in Solutions
On NP-Hardness of $L_1/L_2$ Minimization and Bound Theory of Nonzero Entries in Solutions Open
The \(L_1/L_2\) norm ratio has gained significant attention as a measure of sparsity due to three merits: sharper approximation to the \(L_0\) norm compared to the \(L_1\) norm, being parameter-free and scale-invariant, and exceptional per…
View article: Research and Application on the Microscopic Mechanism of Liquid Extraction in Offshore Conventional Loose Sandstone and Heavy Oil Reservoirs
Research and Application on the Microscopic Mechanism of Liquid Extraction in Offshore Conventional Loose Sandstone and Heavy Oil Reservoirs Open
After years of high efficiency development, offshore conventional heavy oil reservoirs have gradually entered the high water cut stage. Oil well extraction is an important way to achieve stable and increase production and improve the recov…
View article: Heavy-ball-based optimal thresholding algorithms for sparse linear inverse problems
Heavy-ball-based optimal thresholding algorithms for sparse linear inverse problems Open
Linear inverse problems arise in diverse engineering fields especially in signal and image reconstruction. The development of computational methods for linear inverse problems with sparsity is one of the recent trends in this field. The so…
View article: Dynamic Orthogonal Matching Pursuit for Sparse Data Reconstruction
Dynamic Orthogonal Matching Pursuit for Sparse Data Reconstruction Open
The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for sparse data reconstruction or approximation. It acts as a driving force for the development of several other greedy methods for sparse data reconstruction, and i…
View article: Natural Thresholding Algorithms for Signal Recovery with Sparsity
Natural Thresholding Algorithms for Signal Recovery with Sparsity Open
The algorithms based on the technique of optimal $k$-thresholding (OT) were recently proposed for signal recovery, and they are very different from the traditional family of hard thresholding methods. However, the computational cost for OT…
View article: Heavy-Ball-Based Hard Thresholding Algorithms for Sparse Signal Recovery
Heavy-Ball-Based Hard Thresholding Algorithms for Sparse Signal Recovery Open
The hard thresholding technique plays a vital role in the development of algorithms for sparse signal recovery. By merging this technique and heavy-ball acceleration method which is a multi-step extension of the traditional gradient descen…
View article: Partial gradient optimal thresholding algorithms for a class of sparse optimization problems
Partial gradient optimal thresholding algorithms for a class of sparse optimization problems Open
The optimization problems with a sparsity constraint is a class of important global optimization problems. A typical type of thresholding algorithms for solving such a problem adopts the traditional full steepest descent direction or Newto…
View article: Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems
Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems Open
Sparse signals can be possibly reconstructed by an algorithm which merges a traditional nonlinear optimization method and a certain thresholding technique. Different from existing thresholding methods, a novel thresholding technique referr…
View article: Short Range Correlation Transformer for Occluded Person Re-Identification
Short Range Correlation Transformer for Occluded Person Re-Identification Open
Occluded person re-identification is one of the challenging areas of computer vision, which faces problems such as inefficient feature representation and low recognition accuracy. Convolutional neural network pays more attention to the ext…
View article: Natural Thresholding Algorithms for Signal Recovery With Sparsity
Natural Thresholding Algorithms for Signal Recovery With Sparsity Open
The algorithms based on the technique of optimal -thresholding (OT) were recently proposed for signal recovery, and they are very different from the traditional family of hard thresholding methods. However, the computational cost for OT-ba…
View article: Dynamic Orthogonal Matching Pursuit for Sparse Data Reconstruction
Dynamic Orthogonal Matching Pursuit for Sparse Data Reconstruction Open
The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for sparse data reconstruction or approximation. It acts as a driving force for the development of several other greedy methods for sparse data reconstruction, and i…
View article: Dynamic Orthogonal Matching Pursuit for Signal Reconstruction
Dynamic Orthogonal Matching Pursuit for Signal Reconstruction Open
Orthogonal matching pursuit (OMP) is one of the mainstream algorithms for signal reconstruction/approximation. It plays a vital role in the development of compressed sensing theory, and it also acts as a driving force for the development o…
View article: Partial Gradient Optimal Thresholding Algorithms for a Class of Sparse Optimization Problems
Partial Gradient Optimal Thresholding Algorithms for a Class of Sparse Optimization Problems Open
The optimization problems with a sparsity constraint is a class of important global optimization problems. A typical type of thresholding algorithms for solving such a problem adopts the traditional full steepest descent direction or Newto…
View article: Analysis of optimal thresholding algorithms for compressed sensing
Analysis of optimal thresholding algorithms for compressed sensing Open
The optimal k-thresholding (OT) and optimal k-thresholding pursuit (OTP) are newly introduced frameworks of thresholding techniques for compressed sensing and signal approximation. Such frameworks motivate the practical and efficient algor…
View article: Dual-density-based reweighted $$\ell _{1}$$-algorithms for a class of $$\ell _{0}$$-minimization problems
Dual-density-based reweighted $$\ell _{1}$$-algorithms for a class of $$\ell _{0}$$-minimization problems Open
The optimization problem with sparsity arises in many areas of science and engineering such as compressed sensing, image processing, statistical learning and data sparse approximation. In this paper, we study the dual-density-based reweigh…
View article: Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems
Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems Open
Sparse signals can be possibly reconstructed by an algorithm which merges a traditional nonlinear optimization method and a certain thresholding technique. Different from existing thresholding methods, a novel thresholding technique referr…
View article: Newton-Type Optimal Thresholding Algorithms for Sparse Optimization\n Problems
Newton-Type Optimal Thresholding Algorithms for Sparse Optimization\n Problems Open
Sparse signals can be possibly reconstructed by an algorithm which merges a\ntraditional nonlinear optimization method and a certain thresholding technique.\nDifferent from existing thresholding methods, a novel thresholding technique\nref…
View article: Improved RIP-Based Bounds for Guaranteed Performance of Several Compressed Sensing Algorithms
Improved RIP-Based Bounds for Guaranteed Performance of Several Compressed Sensing Algorithms Open
Iterative hard thresholding (IHT) and compressive sampling matching pursuit (CoSaMP) are two types of mainstream compressed sensing algorithms using hard thresholding operators for signal recovery and approximation. The guaranteed performa…
View article: Improved RIP-Based Bounds for Guaranteed Performance of two Compressed Sensing Algorithms
Improved RIP-Based Bounds for Guaranteed Performance of two Compressed Sensing Algorithms Open
Iterative hard thresholding (IHT) and compressive sampling matching pursuit (CoSaMP) are two types of mainstream compressed sensing algorithms using hard thresholding operators for signal recovery and approximation. The guaranteed performa…
View article: Dual-density-based reweighted $\ell_{1}$-algorithms for a class of $\ell_{0}$-minimization problems
Dual-density-based reweighted $\ell_{1}$-algorithms for a class of $\ell_{0}$-minimization problems Open
The optimization problem with sparsity arises in many areas of science and engineering such as compressed sensing, image processing, statistical learning and data sparse approximation. In this paper, we study the dual-density-based reweigh…
View article: On norm compression inequalities for partitioned block tensors
On norm compression inequalities for partitioned block tensors Open
When a tensor is partitioned into subtensors, some tensor norms of these subtensors form a tensor called a norm compression tensor. Norm compression inequalities for tensors focus on the relation of the norm of this compressed tensor to th…
View article: Dual-density-based reweighted 𝓁 1 -algorithms for a class of 𝓁 0 -minimization problems.
Dual-density-based reweighted 𝓁 1 -algorithms for a class of 𝓁 0 -minimization problems. Open
The optimization problem with sparsity arises in many areas of science and engineering such as compressed sensing, image processing, statistical learning and data sparse approximation. In this paper, we study the dual-density-based reweigh…
View article: Newton-Step-Based Hard Thresholding Algorithms for Sparse Signal Recovery
Newton-Step-Based Hard Thresholding Algorithms for Sparse Signal Recovery Open
Sparse signal recovery or compressed sensing can be formulated as certain sparse optimization problems. The classic optimization theory indicates that the Newton-like method often has a numerical advantage over the gradient method for nonl…
View article: Analysis of Optimal Thresholding Algorithms for Compressed Sensing
Analysis of Optimal Thresholding Algorithms for Compressed Sensing Open
The optimal $k$-thresholding (OT) and optimal $k$-thresholding pursuit (OTP) are newly introduced frameworks of thresholding techniques for compressed sensing and signal approximation. Such frameworks motivate the practical and efficient a…
View article: Optimal $k$-thresholding algorithms for sparse optimization problems
Optimal $k$-thresholding algorithms for sparse optimization problems Open
The simulations indicate that the existing hard thresholding technique independent of the residual function may cause a dramatic increase or numerical oscillation of the residual. This inherit drawback of the hard thresholding renders the …
View article: Stability Analysis for a Class of Sparse Optimization Problems
Stability Analysis for a Class of Sparse Optimization Problems Open
The sparse optimization problems arise in many areas of science and engineering, such as compressed sensing, image processing, statistical and machine learning. The $\ell_{0}$-minimization problem is one of such optimization problems, whic…
View article: Half adder and half subtractor logic gates based on nicking enzymes
Half adder and half subtractor logic gates based on nicking enzymes Open
A series of DNA logic devices with simple structure and extremely short reaction time.
View article: A Theoretical Analysis of Sparse Recovery Stability of Dantzig Selector and LASSO
A Theoretical Analysis of Sparse Recovery Stability of Dantzig Selector and LASSO Open
Dantzig selector (DS) and LASSO problems have attracted plenty of attention in statistical learning, sparse data recovery and mathematical optimization. In this paper, we provide a theoretical analysis of the sparse recovery stability of t…