Separable approximations and decomposition methods for the augmented Lagrangian
نویسندگان
چکیده
منابع مشابه
Separable Approximations and Decomposition Methods for the Augmented Lagrangian
In this paper we study decomposition methods based on separable approximations for minimizing the augmented Lagrangian. In particular, we study and compare the Diagonal Quadratic Approximation Method (DQAM) of Mulvey and Ruszczyński [13] and the Parallel Coordinate Descent Method (PCDM) of Richtárik and Takáč [23]. We show that the two methods are equivalent for feasibility problems up to the s...
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A general decomposition framework for large convex optimization problems based on augmented Lagrangians is described. The approach is then applied to multistage stochastic programming problems in two di erent ways: by decomposing the problem into scenarios and by decomposing it into nodes corresponding to stages. Theoretical convergence properties of the two approaches are derived and a computa...
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Abstract. In this paper, we analyze the numerical behaviour of a separable Augmented Lagrangian algorithm with multiple scaling parameters, different parameters associated with each dualized coupling constraint as well as with each subproblem. We show that an optimal superlinear rate of convergence can be theoretically attained in the twice differentiable case and propose an adaptive scaling st...
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ژورنال
عنوان ژورنال: Optimization Methods and Software
سال: 2014
ISSN: 1055-6788,1029-4937
DOI: 10.1080/10556788.2014.966824