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Adaptive Newton-CG methods with global and local analysis for unconstrained optimization with Hölder continuous Hessian
Ziyang Zeng, Junyu Zhang, Chuan He
In this paper, we study Newton-conjugate gradient (Newton-CG) methods for minimizing a nonconvex function whose Hessian is -Hölder continuous with modulus and…
A New Kernel Regularity Condition for Distributed Mirror Descent: Broader Coverage and Simpler Analysis
Junwen Qiu, Ziyang Zeng, Leilei Mei +1
Existing convergence analyses of distributed optimization methods in non-Euclidean geometries typically rely on kernel assumptions: (i) global Lipschitz smoothness and (ii) bi-conv…
Graph-Guided Fused Regularization for Single- and Multi-Task Regression on Spatiotemporal Data
Meixia Lin, Ziyang Zeng, Yangjing Zhang
Spatiotemporal matrix-valued data arise frequently in modern applications, yet performing effective regression analysis remains challenging due to complex, dimension-specific depen…
Multiple Regression for Matrix and Vector Predictors: Models, Theory, Algorithms, and Beyond
Meixia Lin, Ziyang Zeng, Yangjing Zhang
Matrix regression plays an important role in modern data analysis due to its ability to handle complex relationships involving both matrix and vector variables. We propose a class…