Generalized Conditional Gradient with Augmented Lagrangian for Composite Minimization
نویسندگان
چکیده
منابع مشابه
Efficient Generalized Conditional Gradient with Gradient Sliding for Composite Optimization
Generalized conditional gradient method has regained increasing research interest as an alternative to another popular proximal gradient method for sparse optimization problems. For particular tasks, its low computation cost of linear subproblem evaluation on each iteration leads to superior practical performance. However, the inferior iteration complexity incurs excess number of gradient evalu...
متن کاملA Modified Barrier-Augmented Lagrangian Method for Constrained Minimization
We present and analyze an interior-exterior augmented Lagrangian method for solving constrained optimization problems with both inequality and equality constraints. This method, the modified barrier—augmented Lagrangian (MBAL) method, is a combination of the modified barrier and the augmented Lagrangian methods. It is based on the MBAL function, which treats inequality constraints with a modifi...
متن کاملGeneralized Conditional Gradient for Sparse Estimation
Sparsity is an important modeling tool that expands the applicability of convex formulations for data analysis, however it also creates significant challenges for efficient algorithm design. In this paper we investigate the generalized conditional gradient (GCG) algorithm for solving sparse optimization problems—demonstrating that, with some enhancements, it can provide a more efficient alterna...
متن کاملThe proximal augmented Lagrangian method for nonsmooth composite optimization
We study a class of optimization problems in which the objective function is given by the sum of a differentiable but possibly nonconvex component and a nondifferentiable convex regularization term. We introduce an auxiliary variable to separate the objective function components and utilize the Moreau envelope of the regularization term to derive the proximal augmented Lagrangian – a continuous...
متن کاملGeneralized Quadratic Augmented Lagrangian Methods with Nonmonotone Penalty Parameters
For nonconvex optimization problem with both equality and inequality constraints, we introduce a new augmented Lagrangian function and propose the corresponding multiplier algorithm. New iterative strategy on penalty parameter is presented. Different global convergence properties are established depending on whether the penalty parameter is bounded. Even if the iterative sequence {xk} is diverg...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Optimization
سال: 2020
ISSN: 1052-6234,1095-7189
DOI: 10.1137/19m1240460