A5025579166- Advanced Optimization Algorithms Research
- Optimization and Variational Analysis
- Sparse and Compressive Sensing Techniques
- Risk and Portfolio Optimization
- Optimization and Mathematical Programming
- Tensor decomposition and applications
- Matrix Theory and Algorithms
- Probabilistic and Robust Engineering Design
- Fuzzy Systems and Optimization
- Structural Health Monitoring Techniques
- Polynomial and algebraic computation
- Water resources management and optimization
- Advanced Control Systems Optimization
- Complexity and Algorithms in Graphs
- Numerical methods in inverse problems
- Numerical Methods and Algorithms
- Iterative Methods for Nonlinear Equations
- Elasticity and Material Modeling
- Mathematical Inequalities and Applications
- Point processes and geometric inequalities
- Control Systems and Identification
- Fixed Point Theorems Analysis
- Stochastic Gradient Optimization Techniques
- Vehicle Routing Optimization Methods
- Advanced Banach Space Theory
Hangzhou Dianzi University
2025
Monash University Malaysia
2025
UNSW Sydney
2015-2024
Anzac Research Institute
2024
Affiliated Hospital of Guizhou Medical University
2023
Guiyang Medical University
2023
University of Technology Sydney
2021
Shanghai Academy of Spaceflight Technology
2021
Dalat University
2016
Guangdong Police College
2016
We consider the problem of minimizing sum a smooth function $h$ with bounded Hessian, and nonsmooth function. assume that latter is composition proper closed $P$ surjective linear map $\cal M$, proximal mappings $\tau P$, > 0$, simple to compute. This nonconvex in general encompasses many important applications engineering machine learning. In this paper, we examined two types splitting methods for solving optimization problem: alternating direction method multipliers gradient algorithm. For...
Duality theory has played a key role in convex programming the absence of data uncertainty. In this paper, we present duality for problems face uncertainty via robust optimization. We characterize strong between counterpart an uncertain program and optimistic its Lagrangian dual. provide new characteristic cone constraint qualification which is necessary sufficient sense that holds if only every objective function program. further show always polyhedral by verifying our qualification, where...
SUMMARY In this paper, using variational analysis and optimization techniques, we examine some fundamental analytic properties of Z ‐eigenvalues a real symmetric tensor with even order. We first establish that the maximum ‐eigenvalue function is continuous convex on space so provide formulas conjugate ε ‐subdifferential function. Consequently, for an m th‐order N ‐dimensional , show normalized eigenspace associated ρ Hölder stable at . As by‐product, also always least semismooth application,...
In real-world engineering, uncertainty is ubiquitous within material properties, structural geometry, load conditions, and the like. These uncertainties have substantial impacts on estimation of performance. Furthermore, information or datasets in real life commonly contain imperfections, e.g., noise, outliers, missing data. To quantify these induced by behaviours reduce effects data imperfections simultaneously, a machine learning-aided stochastic analysis framework proposed. A novel...
This paper is mainly devoted to the study and applications of Hölder metric subregularity (or $q$-subregularity order $q\in(0,1]$) for general set-valued mappings between infinite-dimensional spaces. Employing advanced techniques variational analysis generalized differentiation, we derive neighborhood point-based sufficient conditions as well necessary $q$-metric with evaluating exact bound, which are new even conventional (first-order) in both finite infinite dimensions. In this way also...
We establish alternative theorems for quadratic inequality systems. Consequently, we obtain Lagrange multiplier characterizations of global optimality classes nonconvex optimization problems. present a generalization Dine's theorem to system two homogeneous functions with regular cone. The class cones are K which $(K\cup-K)$ is subspace. As consequence, the powerful S-lemma, paves way complete characterization general model problem involving equality constraints in addition single...
In this paper, we study the rate of convergence cyclic projection algorithm applied to finitely many basic semialgebraic convex sets. We establish an explicit estimate which relies on maximum degree polynomials that generate sets and dimension underlying space. achieve our results by exploiting algebraic structure
In this paper, we establish sublinear and linear convergence of fixed point iterations generated by averaged operators in a Hilbert space. Our results are achieved under bounded H\"older regularity assumption which generalizes the well-known notion regularity. As an application our results, provide rate analysis for Krasnoselskii-Mann iterations, cyclic projection algorithm, Douglas-Rachford feasibility algorithm along with some variants. important case underlying sets convex described...
The multiobjective optimization model studied in this paper deals with simultaneous minimization of finitely many linear functions subject to an arbitrary number uncertain constraints. We first provide a radius robust feasibility guaranteeing the counterpart under affine data parametrization. then establish dual characterizations solutions our that are immunized against uncertainty by way characterizing corresponding model. Consequently, we present duality theorems relating value its problem.
Higher-order sensor networks are more accurate in characterizing the nonlinear dynamics of sensory time-series data modern industrial settings by allowing multi-node connections beyond simple pairwise graph edges. In light this, we propose a deep spatio-temporal hypergraph convolutional neural network for soft sensing (ST-HCSS). particular, our proposed framework is able to construct and leverage higher-order (hypergraph) model complex multi-interactions between nodes absence prior...