A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems
We develop a fast and robust algorithm for solving large-scale convex composite
optimization models with an emphasis on the \ell_1-regularized least squares regression …
optimization models with an emphasis on the \ell_1-regularized least squares regression …
Stochastic first-order methods for convex and nonconvex functional constrained optimization
Functional constrained optimization is becoming more and more important in machine
learning and operations research. Such problems have potential applications in risk-averse …
learning and operations research. Such problems have potential applications in risk-averse …
On efficiently solving the subproblems of a level-set method for fused lasso problems
In applying the level-set method developed in [E. Van den Berg and MP Friedlander, SIAM J.
Sci. Comput., 31 (2008), pp. 890--912] and [E. Van den Berg and MP Friedlander, SIAM J …
Sci. Comput., 31 (2008), pp. 890--912] and [E. Van den Berg and MP Friedlander, SIAM J …
First-order methods for constrained convex programming based on linearized augmented Lagrangian function
Y Xu - INFORMS Journal on Optimization, 2021 - pubsonline.informs.org
First-order methods (FOMs) have been popularly used for solving large-scale problems.
However, many existing works only consider unconstrained problems or those with simple …
However, many existing works only consider unconstrained problems or those with simple …
Robust Low-Rank Tensor Minimization via a New Tensor Spectral -Support Norm
J Lou, YM Cheung - IEEE Transactions on Image Processing, 2019 - ieeexplore.ieee.org
Recently, based on a new tensor algebraic framework for third-order tensors, the tensor
singular value decomposition (t-SVD) and its associated tubal rank definition have shed new …
singular value decomposition (t-SVD) and its associated tubal rank definition have shed new …
First-order methods for nonsmooth nonconvex functional constrained optimization with or without slater points
Constrained optimization problems where both the objective and constraints may be
nonsmooth and nonconvex arise across many learning and data science settings. In this …
nonsmooth and nonconvex arise across many learning and data science settings. In this …
A level-set method for convex optimization with a feasible solution path
Large-scale constrained convex optimization problems arise in several application domains.
First-order methods are good candidates to tackle such problems due to their low iteration …
First-order methods are good candidates to tackle such problems due to their low iteration …
Level-set methods for finite-sum constrained convex optimization
We consider the constrained optimization where the objective function and the constraints
are defined as summation of finitely many loss functions. This model has applications in …
are defined as summation of finitely many loss functions. This model has applications in …
Foundations of gauge and perspective duality
We revisit the foundations of gauge duality and demonstrate that it can be explained using a
modern approach to duality based on a perturbation framework. We therefore put gauge …
modern approach to duality based on a perturbation framework. We therefore put gauge …
An accelerated variance reduced extra-point approach to finite-sum vi and optimization
In this paper, we develop stochastic variance reduced algorithms for solving a class of finite-
sum monotone VI, where the operator consists of the sum of finitely many monotone VI …
sum monotone VI, where the operator consists of the sum of finitely many monotone VI …