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An augmented Lagrangian interior-point approach for large-scale NLP problems on graphics processing units.

Y. Cao, A. Seth, and C. Laird. Computers & Chemical Engineering, (2016)

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Scalable modeling and solution of stochastic multiobjective optimization problems.Y. Cao, L. Fuentes-Cortés, S. Chen, and V. Zavala. Comput. Chem. Eng., (2017)Deep Learning-based Predictive Control of Battery Management for Frequency Regulation.Y. Li, Y. Wang, Y. Chen, K. Hua, J. Ren, G. Mozafari, Q. Lu, and Y. Cao. CoRR, (2022)An interior-point method for efficient solution of block-structured NLP problems using an implicit Schur-complement decomposition.J. Kang, Y. Cao, D. Word, and C. Laird. Comput. Chem. Eng., (2014)Clustering-based preconditioning for stochastic programs.Y. Cao, C. Laird, and V. Zavala. Comp. Opt. and Appl., 64 (2): 379-406 (2016)An augmented Lagrangian interior-point approach for large-scale NLP problems on graphics processing units.Y. Cao, A. Seth, and C. Laird. Computers & Chemical Engineering, (2016)Graph-based modeling and simulation of complex systems.J. Jalving, Y. Cao, and V. Zavala. Computers & Chemical Engineering, (2019)A scalable global optimization algorithm for stochastic nonlinear programs.Y. Cao, and V. Zavala. J. Global Optimization, 75 (2): 393-416 (2019)Multi-objective transient peak shaving optimization of a gas pipeline system under demand uncertainty.Q. Chen, C. Wu, L. Zuo, M. Mehrtash, Y. Wang, Y. Bu, R. Sadiq, and Y. Cao. Comput. Chem. Eng., (2021)

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An augmented Lagrangian interior-point approach for large-scale NLP problems on graphics processing units.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML An augmented Lagrangian interior-point approach for large-scale NLP problems on graphics processing units.

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An augmented Lagrangian interior-point approach for large-scale NLP problems on graphics processing units.