  Sequential quadratic programming (SQP) is widely used in solving nonlinear
optimization problem, with advantages of warm-starting solutions, as well as
finding high-accurate solution and converging quadratically using second-order
information, such as the Hessian matrix. In this study we develop a scalable
SQP algorithm for solving the alternate current optimal power flow problem
(ACOPF), leveraging the parallel computing capabilities of graphics processing
units (GPUs). Our methodology incorporates the alternating direction method of
multipliers (ADMM) to initialize and decompose the quadratic programming
subproblems within each SQP iteration into independent small subproblems for
each electric grid component. We have implemented the proposed SQP algorithm
using our portable Julia package ExaAdmm.jl, which solves the ADMM subproblems
in parallel on all major GPU architectures. For numerical experiments, we
compared three solution approaches: (i) the SQP algorithm with a GPU-based ADMM
subproblem solver, (ii) a CPU-based ADMM solver, and (iii) the QP solver Ipopt
(the state-of-the-art interior point solver) and observed that for larger
instances our GPU-based SQP solver efficiently leverages the GPU many-core
architecture, dramatically reducing the solution time.
