35 citations · 81 across the 17 of their papers we have counts for
17 papers
A Block Coordinate Descent Method for Nonsmooth Composite Optimization under Orthogonality Constraints
Ganzhao Yuan
Nonsmooth composite optimization with orthogonality constraints has a wide range of applications in statistical learning and data science. However, this problem is challenging due…
Coordinate Descent Methods for Fractional Minimization
Ganzhao Yuan
We consider a class of structured fractional minimization problems, in which the numerator part of the objective is the sum of a differentiable convex function and a convex non-smo…
Coordinate Descent Methods for DC Minimization: Optimality Conditions and Global Convergence
Ganzhao Yuan
Difference-of-Convex (DC) minimization, referring to the problem of minimizing the difference of two convex functions, has been found rich applications in statistical learning and…
Smoothing Proximal Gradient Methods for Nonsmooth Sparsity Constrained Optimization: Optimality Conditions and Global Convergence
Ganzhao Yuan
Nonsmooth sparsity constrained optimization encompasses a broad spectrum of applications in machine learning. This problem is generally non-convex and NP-hard. Existing solutions t…
A Block Decomposition Algorithm for Sparse Optimization
Ganzhao Yuan, Li Shen, Wei-Shi Zheng
Sparse optimization is a central problem in machine learning and computer vision. However, this problem is inherently NP-hard and thus difficult to solve in general. Combinatorial…
A Generalized Matrix Splitting Algorithm
Ganzhao Yuan, Wei-Shi Zheng, Li Shen +1
Composite function minimization captures a wide spectrum of applications in both computer vision and machine learning. It includes bound constrained optimization, norm reg…
L0TV: A Sparse Optimization Method for Impulse Noise Image Restoration
Ganzhao Yuan, Bernard Ghanem
Total Variation (TV) is an effective and popular prior model in the field of regularization-based image processing. This paper focuses on total variation for removing impulse noise…
A Decomposition Algorithm for the Sparse Generalized Eigenvalue Problem
Ganzhao Yuan, Li Shen, Wei-Shi Zheng
The sparse generalized eigenvalue problem arises in a number of standard and modern statistical learning models, including sparse principal component analysis, sparse Fisher discri…
A Coordinate-wise Optimization Algorithm for Sparse Inverse Covariance Selection
Ganzhao Yuan, Haoxian Tan, Wei-Shi Zheng
Sparse inverse covariance selection is a fundamental problem for analyzing dependencies in high dimensional data. However, such a problem is difficult to solve since it is NP-hard.…
A Hybrid Method of Combinatorial Search and Coordinate Descent for Discrete Optimization
Ganzhao Yuan, Li Shen, Wei-Shi Zheng
Discrete optimization is a central problem in mathematical optimization with a broad range of applications, among which binary optimization and sparse optimization are two common o…
A Matrix Splitting Method for Composite Function Minimization
Ganzhao Yuan, Wei-Shi Zheng, Bernard Ghanem
Composite function minimization captures a wide spectrum of applications in both computer vision and machine learning. It includes bound constrained optimization and cardinality re…
Sparsity Constrained Minimization via Mathematical Programming with Equilibrium Constraints
Ganzhao Yuan, Bernard Ghanem
Sparsity constrained minimization captures a wide spectrum of applications in both machine learning and signal processing. This class of problems is difficult to solve since it is…
Binary Optimization via Mathematical Programming with Equilibrium Constraints
Ganzhao Yuan, Bernard Ghanem
Binary optimization is a central problem in mathematical optimization and its applications are abundant. To solve this problem, we propose a new class of continuous optimization te…
Convex Optimization for Linear Query Processing under Approximate Differential Privacy
Ganzhao Yuan, Yin Yang, Zhenjie Zhang +1
Differential privacy enables organizations to collect accurate aggregates over sensitive data with strong, rigorous guarantees on individuals' privacy. Previous work has found that…
Optimizing Batch Linear Queries under Exact and Approximate Differential Privacy
Ganzhao Yuan, Zhenjie Zhang, Marianne Winslett +3
Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, suc…
Low Rank Mechanism for Optimizing Batch Queries under Differential Privacy
Ganzhao Yuan, Zhenjie Zhang, Marianne Winslett +3
Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, suc…
Low-Rank Mechanism: Optimizing Batch Queries under Differential Privacy
Ganzhao Yuan, Zhenjie Zhang, Marianne Winslett +3
Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, suc…