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- W4226389402 abstract "This paper presents an efficient method for extracting the second-order sensitivities from a system of implicit nonlinear equations on upcoming graphical processing units (GPU) dominated computer systems. We design a custom automatic differentiation (AutoDiff) backend that targets highly parallel architectures by extracting the second-order information in batch. When the nonlinear equations are associated to a reduced space optimization problem, we leverage the parallel reverse-mode accumulation in a batched adjoint-adjoint algorithm to compute efficiently the reduced Hessian of the problem. We apply the method to extract the reduced Hessian associated to the balance equations of a power network, and show on the largest instances that a parallel GPU implementation is 30 times faster than a sequential CPU reference based on UMFPACK." @default.
- W4226389402 created "2022-05-05" @default.
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- W4226389402 date "2022-01-01" @default.
- W4226389402 modified "2023-09-27" @default.
- W4226389402 title "Batched Second-Order Adjoint Sensitivity for Reduced Space Methods" @default.
- W4226389402 doi "https://doi.org/10.48550/arxiv.2201.00241" @default.
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