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04/25/2023
A New Inexact Proximal Linear Algorithm with Adaptive Stopping Criteria for Robust Phase Retrieval
This paper considers the robust phase retrieval problem, which can be ca...
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11/03/2022
A Riemannian ADMM
We consider a class of Riemannian optimization problems where the object...
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10/23/2022
Decentralized Stochastic Bilevel Optimization with Improved Per-Iteration Complexity
Bilevel optimization recently has received tremendous attention due to i...
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06/12/2022
Federated Learning on Riemannian Manifolds
Federated learning (FL) has found many important applications in smart-p...
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02/08/2022
Efficiently Escaping Saddle Points in Bilevel Optimization
Bilevel optimization is one of the fundamental problems in machine learn...
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09/29/2021
On the Convergence of Projected Alternating Maximization for Equitable and Optimal Transport
This paper studies the equitable and optimal transport (EOT) problem, wh...
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04/09/2021
A Riemannian smoothing steepest descent method for non-Lipschitz optimization on submanifolds
In this paper, we propose a Riemannian smoothing steepest descent method...
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02/05/2021
Projection Robust Wasserstein Barycenter
Collecting and aggregating information from several probability measures...
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12/09/2020
A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance
The Wasserstein distance has become increasingly important in machine le...
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08/18/2020
Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method
Robust low-rank matrix completion (RMC), or robust principal component a...
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07/18/2020
A Manifold Proximal Linear Method for Sparse Spectral Clustering with Application to Single-Cell RNA Sequencing Data Analysis
Spectral clustering is one of the fundamental unsupervised learning meth...
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05/05/2020
Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning
We consider the problem of maximizing the ℓ_1 norm of a linear map over ...
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05/03/2020
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold
Riemannian optimization has drawn a lot of attention due to its wide app...
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03/25/2020
Zeroth-order Optimization on Riemannian Manifolds
We propose and analyze zeroth-order algorithms for optimization over Rie...
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01/22/2020
Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities
In this paper, we study zeroth-order algorithms for minimax optimization...
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03/27/2019
An Alternating Manifold Proximal Gradient Method for Sparse PCA and Sparse CCA
Sparse principal component analysis (PCA) and sparse canonical correlati...
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11/03/2018
Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity
Regularized empirical risk minimization problem with linear predictor ap...
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11/02/2018
Proximal Gradient Method for Manifold Optimization
This paper considers manifold optimization problems with nonsmooth and n...
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06/09/2018
Efficient Optimization Algorithms for Robust Principal Component Analysis and Its Variants
Robust PCA has drawn significant attention in the last decade due to its...
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02/06/2018
Highly accurate model for prediction of lung nodule malignancy with CT scans
Computed tomography (CT) examinations are commonly used to predict lung ...
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10/05/2017
Primal-Dual Optimization Algorithms over Riemannian Manifolds: an Iteration Complexity Analysis
In this paper we study nonconvex and nonsmooth multi-block optimization ...
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12/29/2016
Geometric descent method for convex composite minimization
In this paper, we extend the geometric descent method recently proposed ...
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07/05/2016
Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization
In this paper we study stochastic quasi-Newton methods for nonconvex sto...
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05/13/2016
Barzilai-Borwein Step Size for Stochastic Gradient Descent
One of the major issues in stochastic gradient descent (SGD) methods is ...
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05/09/2016
Structured Nonconvex and Nonsmooth Optimization: Algorithms and Iteration Complexity Analysis
Nonconvex and nonsmooth optimization problems are frequently encountered...
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05/16/2015
Global Convergence of Unmodified 3-Block ADMM for a Class of Convex Minimization Problems
The alternating direction method of multipliers (ADMM) has been successf...
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01/27/2013
An Extragradient-Based Alternating Direction Method for Convex Minimization
In this paper, we consider the problem of minimizing the sum of two conv...
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10/30/2010
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Gaussian graphical models are of great interest in statistical learning....
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12/23/2009