Paper Info
Title
Distributed Algorithm Via Continuously Differentiable Exact Penalty Method For Network Optimization
Abstract
This paper proposes a distributed optimization framework for solving nonlinear programming problems with separable objective function and local constraints. Our novel approach is based on first reformulating the original problem as an unconstrained optimization problem using continuously differentiable exact penalty function methods and then using gradient based optimization algorithms. The reformulation is based on replacing the Lagrange multipliers in the augmented Lagrangian of the original problem with Lagrange multiplier functions. The problem of calculating the gradient of the penalty function is challenging as it is non-distributed in general even if the original problem is distributed. We show that we can reformulate this problem as a distributed, unconstrained convex optimization problem. The proposed framework opens new opportunities for the application of various distributed algorithms designed for unconstrained optimization.
Year
DOI
Venue
2018
10.1109/CDC.2018.8619651
2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
Field
DocType
ISSN
Mathematical optimization,Computer science,Lagrange multiplier,Nonlinear programming,Augmented Lagrangian method,Distributed algorithm,Smoothness,Convex optimization,Optimization problem,Penalty method
Conference
0743-1546
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Priyank Srivastava102.03
Jorge Cortes21046113.95