On scaled stopping criteria for a safeguarded augmented Lagrangian method with theoretical guarantees.
Roberto AndreaniGabriel HaeserMaría Laura SchuverdtLeonardo D. SecchinPaulo J. S. SilvaPublished in: Math. Program. Comput. (2022)
Keyphrases
- theoretical guarantees
- stopping criteria
- augmented lagrangian method
- convex optimization
- augmented lagrangian
- total variation
- total variation regularization
- constrained minimization
- worst case
- global convergence
- clustering algorithm
- image denoising
- constrained optimization
- locally adaptive
- constrained optimization problems
- stopping criterion
- image deblurring
- image restoration
- primal dual
- policy iteration
- denoising
- objective function
- loss function
- linear programming
- evolutionary algorithm
- computer vision