We analyze the convergence properties of the consensus-alternating direction method of multipliers (ADMM) for solving general quadratically constrained quadratic programs. We prove that the augmented Lagrangian function value is monotonically non-increasing as long as the augmented Lagrangian parameter is chosen to be sufficiently large. Simulation results show that the augmented Lagrangian function is bounded from below when the matrix in the quadratic term of the objective function is positive definite. In such a case, the consensus-ADMM is convergent.Comment: 13 pages, 5 figure
In a recent paper, Ling et al. investigated the over-parametrized Deep Equilibrium Model (DEQ) with ...
Abstract—In decentralized consensus optimization, a connected network of agents collaboratively mini...
Thanks to its versatility, its simplicity, and its fast convergence, alternating direction method of...
The alternating direction method of multipliers (ADMM) is widely used to solve large-scale linearly ...
Convex quadratic programming (QP) is an important sub-field of mathematical optimization. The altern...
Abstract In this paper, we analyze the convergence of Alternating Direction Method of Multipliers (A...
The framework of Integral Quadratic Constraints (IQCs) is used to present a performance analysis for...
International audienceThis paper analyses the behavior of the augmented Lagrangian algorithm when it...
Sequential quadratic programming (SQP) methods for nonlinearly constrained op-timization typically u...
The classical convergence theory of the augmented Lagrangian method has been developed under the ass...
Mathematical programs with complementarity constraints (MPCCs) are difficult optimization problems t...
This paper investigates the weighted-averaging dynamic for unconstrained and constrained consensus p...
The alternating direction method of multipliers (ADMM) has been widely used for solving struc-tured ...
A variant of the augmented Lagrangian-type algorithm for strictly convex quadratic programming probl...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/87...
In a recent paper, Ling et al. investigated the over-parametrized Deep Equilibrium Model (DEQ) with ...
Abstract—In decentralized consensus optimization, a connected network of agents collaboratively mini...
Thanks to its versatility, its simplicity, and its fast convergence, alternating direction method of...
The alternating direction method of multipliers (ADMM) is widely used to solve large-scale linearly ...
Convex quadratic programming (QP) is an important sub-field of mathematical optimization. The altern...
Abstract In this paper, we analyze the convergence of Alternating Direction Method of Multipliers (A...
The framework of Integral Quadratic Constraints (IQCs) is used to present a performance analysis for...
International audienceThis paper analyses the behavior of the augmented Lagrangian algorithm when it...
Sequential quadratic programming (SQP) methods for nonlinearly constrained op-timization typically u...
The classical convergence theory of the augmented Lagrangian method has been developed under the ass...
Mathematical programs with complementarity constraints (MPCCs) are difficult optimization problems t...
This paper investigates the weighted-averaging dynamic for unconstrained and constrained consensus p...
The alternating direction method of multipliers (ADMM) has been widely used for solving struc-tured ...
A variant of the augmented Lagrangian-type algorithm for strictly convex quadratic programming probl...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/87...
In a recent paper, Ling et al. investigated the over-parametrized Deep Equilibrium Model (DEQ) with ...
Abstract—In decentralized consensus optimization, a connected network of agents collaboratively mini...
Thanks to its versatility, its simplicity, and its fast convergence, alternating direction method of...