Improving ultimate convergence of an augmented Lagrangian method
نویسندگان
چکیده
منابع مشابه
Improving ultimate convergence of an augmented Lagrangian method
Optimization methods that employ the classical Powell-Hestenes-Rockafellar Augmented Lagrangian are useful tools for solving Nonlinear Programming problems. Their reputation decreased in the last ten years due to the comparative success of Interior-Point Newtonian algorithms, which are asymptotically faster. In the present research a combination of both approaches is evaluated. The idea is to p...
متن کاملNew Convergence Properties of the Primal Augmented Lagrangian Method
and Applied Analysis 3 Given x, λ, μ, c , the augmented Lagrangian relaxation problem associated with the augmented Lagrangian L is defined by min L ( x, λ, μ, c ) s.t. x ∈ Ω. Lλ,μ,c Given ε ≥ 0, then the ε-optimal solution set of Lλ,μ,c , denoted by S∗ λ, μ, c, ε , is defined as { x ∈ Ω | Lx, λ, μ, c ≤ inf x∈Ω L ( x, λ, μ, c ) ε } . 2.2 IfΩ is closed and bounded, then the global optimal soluti...
متن کاملOptimality properties of an Augmented Lagrangian method
Sometimes, the feasible set of an optimization problem that one aims to solve using a Nonlinear Programming algorithm is empty. In this case, two characteristics of the algorithm are desirable. On the one hand, the algorithm should converge to a minimizer of some infeasibility measure. On the other hand, one may wish to find a point with minimal infeasibility for which some optimality condition...
متن کاملAn augmented Lagrangian method for distributed optimization
We propose a novel distributed method for convex optimization problems with a certain separability structure. The method is based on the augmented Lagrangian framework. We analyze its convergence and provide an application to two network models, as well as to a two-stage stochastic optimization problem. The proposed method compares favorably to two augmented Lagrangian decomposition methods kno...
متن کاملAugmented Lagrangian Filter Method∗
We introduce a filter mechanism to force convergence for augmented Lagrangian methods for nonlinear programming. In contrast to traditional augmented Lagrangian methods, our approach does not require the use of forcing sequences that drive the first-order error to zero. Instead, we employ a filter to drive the optimality measures to zero. Our algorithm is flexible in the sense that it allows fo...
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: Optimization Methods and Software
سال: 2008
ISSN: 1055-6788,1029-4937
DOI: 10.1080/10556780701577730