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A new inexact alternating directions method for monotone variational inequalities
TLDR
The alternating directions method (ADM) is an effective method for solving a class of variational inequalities (VI) when the proximal and penalty parameters in sub-VI problems are properly selected. Expand
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A Note on the Alternating Direction Method of Multipliers
TLDR
We consider the linearly constrained separable convex programming, whose objective function is separable into m individual convex functions without coupled variables. Expand
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A new alternating direction method for co-coercive variational inequality problems
TLDR
This paper presents a new alternating direction method for solving co-coercive variational inequality problems, where the feasible set is the intersection of a simple set and polyhedron defined by a system of linear equations. Expand
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Linear Rate Convergence of the Alternating Direction Method of Multipliers for Convex Composite Programming
TLDR
In this paper, we aim to prove the linear rate convergence of the alternating direction method of multipliers (ADMM) for solving linearly constrained convex composite optimization problems. Expand
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Convergence Analysis of Douglas-Rachford Splitting Method for "Strongly + Weakly" Convex Programming
TLDR
We consider the convergence of the Douglas--Rachford splitting method (DRSM) for minimizing the sum of a strongly convex function and a weakly convex functions in the “strongly + weakly” convex setting. Expand
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The multi-class, multi-criterion traffic equilibrium and the efficiency of congestion pricing
We consider the effect of the so-called second-best tolls on the price of anarchy of the traffic equilibrium problem where there are multiple classes of users with a discrete set of values of time.Expand
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Local Linear Convergence of the Alternating Direction Method of Multipliers for Quadratic Programs
TLDR
The Douglas--Rachford alternating direction alternating direction method of multipliers has been widely used in various areas. Expand
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New ALS Methods With Extrapolating Search Directions and Optimal Step Size for Complex-Valued Tensor Decompositions
TLDR
In signal processing, data analysis and scientific computing, one often encounters the problem of decomposing a tensor into a sum of contributions. Expand
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A new accuracy criterion for approximate proximal point algorithms
In this paper, we give a new accuracy criterion for approximate proximal point algorithms. The criterion depends on the current iterate and is easy to verify. Under the suggested enforceable accuracyExpand
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On the convergence of the direct extension of ADMM for three-block separable convex minimization models with one strongly convex function
TLDR
We show that when one function in the objective is strongly convex, the penalty parameter and the operators in the linear equality constraint are appropriately restricted, it is sufficient to guarantee the convergence of the direct extension of ADMM. Expand
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