5 papers
Rank-Adaptive and Linearly Convergent Frank--Wolfe Method over Spectrahedron via Nonconvex Oracle
Houduo Qi, Haoning Wang, Liping Zhang
For Frank--Wolfe (FW) methods for convex optimization over the spectrahedron, it remains open whether a block-update variant can be linearly convergent when the update rank never e…
Low-Rank Tensor Completion using Tensor Train Decomposition via Riemannian Optimization on the Quotient Geometry
Zhenlong Hu, Zhongming Chen, Defeng Sun +1
Owing to the effectiveness of Tensor Train (TT) decomposition in managing high-order tensors, low-rank tensor completion within the TT-format has emerged as a prominent research fo…
Simplex Frank-Wolfe: Linear Convergence and Its Numerical Efficiency for Convex Optimization over Polytopes
Haoning Wang, Houduo Qi, Liping Zhang
We investigate variants of the Frank-Wolfe (FW) algorithm for smoothing and strongly convex optimization over polyhedral sets, with the goal of designing algorithms that achieve li…
Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition
Zhenting Luan, Defeng Sun, Haoning Wang +1
Real-time prediction plays a vital role in various control systems, such as traffic congestion control and wireless channel resource allocation. In these scenarios, the predictor u…
Low-rank Tensor Autoregressive Predictor for Third-Order Time-Series Forecasting
Haoning Wang, Liping Zhang
Recently, tensor time-series forecasting has gained increasing attention, whose core requirement is how to perform dimensionality reduction. In this paper, we establish a least squ…