# A Globally Convergent Distributed Jacobi Scheme for Block-Structured Nonconvex Constrained Optimization Problems

@inproceedings{Subramanyam2021AGC, title={A Globally Convergent Distributed Jacobi Scheme for Block-Structured Nonconvex Constrained Optimization Problems}, author={Anirudh Subramanyam and Youngdae Kim and Michel Schanen and F. Pacaud and Mihai Anitescu}, year={2021} }

—Motivated by the increasing availability of high- performance parallel computing, we design a distributed parallel algorithm for linearly-coupled block-structured nonconvex con- strained optimization problems. Our algorithm performs Jacobi-type proximal updates of the augmented Lagrangian function, requiring only local solutions of separable block nonlinear programming (NLP) problems. We provide a cheap and explic- itly computable Lyapunov function that allows us to establish global and local…

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Articles Targeting Exascale with Julia on GPUs for multiperiod optimization with scenario constraints M Anitescu, K Kim, Y Kim, A Maldonado, F Pacaud, V Rao, M Schanen, S Shin, A Subramanyam. . . . .…

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