1 citations · 1 across the 8 of their papers we have counts for
9 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…
On subspace-constrained preconditioning for randomized iterative methods
Yonghan Sun, Hou-Duo Qi, Deren Han +1
In this paper, we further investigate and refine the subspace-constrained preconditioning technique to enhance the theoretical and numerical convergence properties of randomized it…
Local Duality for Sparse Support Vector Machines
Penghe Zhang, Naihua Xiu, Houduo Qi
Due to the rise of cardinality minimization in optimization, sparse support vector machines (SSVMs) have attracted much attention lately and show certain empirical advantages over…
A Robust EDM Optimization Approach for 3D Single-Source Localization with Angle and Range Measurements
Mingyu Zhao, Qingna Li, Hou-Duo Qi
Accurate source localization in Multi-Platform Radar Networks (MPRNs) benefits from exploiting both range and angle measurements under robust estimation. In this paper, we propose…
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…
Composite Optimization with Indicator Functions: Stationary Duality and a Semismooth Newton Method
Penghe Zhang, Naihua Xiu, Houduo Qi
Indicator functions of taking values of zero or one are essential to numerous applications in machine learning and statistics. The corresponding primal optimization model has been…
Analytic analysis of the worst-case complexity of the gradient method with exact line search and the Polyak stepsize
Ya-Kui Huang, Hou-Duo Qi
We give a novel analytic analysis of the worst-case complexity of the gradient method with exact line search and the Polyak stepsize, respectively, which previously could only be e…
Sparse SVM with Hard-Margin Loss: a Newton-Augmented Lagrangian Method in Reduced Dimensions
Penghe Zhang, Naihua Xiu, Hou-Duo Qi
The hard margin loss function has been at the core of the support vector machine (SVM) research from the very beginning due to its generalization capability.On the other hand, the…
iNALM: An inexact Newton Augmented Lagrangian Method for Zero-One Composite Optimization
Penghe Zhang, Naihua Xiu, Hou-Duo Qi
Zero-One Composite Optimization (0/1-COP) is a prototype of nonsmooth, nonconvex optimization problems and it has attracted much attention recently. The augmented Lagrangian Method…