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Solution of Large-scale Structured Optimization Problems with Schur-complement and Augmented Lagrangian Decomposition Methods
2019
In this dissertation we develop numerical algorithms and software tools to facilitate parallel solutions of nonlinear programming (NLP) problems. In particular, we address large-scale, block-structured problems with an intrinsic decomposable configuration. These problems arise in a great number of engineering applications, including parameter estimation, optimal control, network optimization, and stochastic programming. The structure of these problems can be leveraged by optimization solvers to
doi:10.25394/pgs.8210243.v1
fatcat:i5qjpy3upjc7jalnrw7m4eye5a