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07/19/2023
Convergence Guarantees for Stochastic Subgradient Methods in Nonsmooth Nonconvex Optimization
In this paper, we investigate the convergence properties of the stochast...
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06/26/2023
Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
The widely used stochastic gradient methods for minimizing nonconvex com...
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05/06/2023
Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees
In this paper, we present a comprehensive study on the convergence prope...
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12/06/2022
CDOpt: A Python Package for a Class of Riemannian Optimization
Optimization over the embedded submanifold defined by constraints c(x) =...
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09/13/2022
Tractable hierarchies of convex relaxations for polynomial optimization on the nonnegative orthant
We consider polynomial optimization problems (POP) on a semialgebraic se...
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08/16/2022
On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models
In this paper we study the computation of the nonparametric maximum like...
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04/29/2022
Escaping Spurious Local Minima of Low-Rank Matrix Factorization Through Convex Lifting
This work proposes a rapid global solver for nonconvex low-rank matrix f...
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05/28/2021
STRIDE along Spectrahedral Vertices for Solving Large-Scale Rank-One Semidefinite Relaxations
We consider solving high-order semidefinite programming (SDP) relaxation...
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10/22/2020
Learning Graph Laplacian with MCP
Motivated by the observation that the ability of the ℓ_1 norm in promoti...
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04/17/2020
Estimation of sparse Gaussian graphical models with hidden clustering structure
Estimation of Gaussian graphical models is important in natural science ...
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02/26/2020
Efficient algorithms for multivariate shape-constrained convex regression problems
Shape-constrained convex regression problem deals with fitting a convex ...
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03/27/2019
A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems
In this paper, we consider high-dimensional nonconvex square-root-loss r...
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02/01/2019
On the Closed-form Proximal Mapping and Efficient Algorithms for Exclusive Lasso Models
The exclusive lasso regularization based on the ℓ_1,2 norm has become po...
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10/04/2018
Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm
Clustering is a fundamental problem in unsupervised learning. Popular me...
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09/12/2018
A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters
In this paper, we consider the problem of computing a Wasserstein baryce...
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08/22/2018
Efficient sparse Hessian based algorithms for the clustered lasso problem
We focus on solving the clustered lasso problem, which is a least square...
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02/20/2018
An Efficient Semismooth Newton Based Algorithm for Convex Clustering
Clustering may be the most fundamental problem in unsupervised learning ...
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09/24/2016
Max-Norm Optimization for Robust Matrix Recovery
This paper studies the matrix completion problem under arbitrary samplin...
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09/06/2013