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scopus(5)
Characterizing the pbkp_redictive accuracy of dynamic mode decomposition for data-driven control
Conference ObjectAbstract: Dynamic mode decomposition (DMD) is a versatile approach that enables the construction of low-orderPalabras claves:control, DATA, Dynamic mode decomposition, error bounds, Low-order modelsAutores:Lu Q., Shin S., Víctor M. ZavalaFuentes:scopusA Julia Framework for Graph-Structured Nonlinear Optimization
ArticleAbstract: Graph theory provides a convenient framework for modeling and solving structured optimization problePalabras claves:Autores:Cole D.L., Shin S., Víctor M. ZavalaFuentes:scopusA graph-based modeling abstraction for optimization: concepts and implementation in Plasmo.jl
ArticleAbstract: We present a general graph-based modeling abstraction for optimization that we call an OptiGraph. UnPalabras claves:decomposition, Graph Theory, Modeling, Optimization, StructureAutores:Jalving J., Shin S., Víctor M. ZavalaFuentes:scopusControllability and observability imply exponential decay of sensitivity in dynamic optimization
Conference ObjectAbstract: We study a property of dynamic optimization (DO) problems (as those encountered in model pbkp_redictPalabras claves:Model Pbkp_redictive Control, Moving horizon estimation, Nonlinear, Sensitivity AnalysisAutores:Shin S., Víctor M. ZavalaFuentes:scopusEXPONENTIAL DECAY OF SENSITIVITY IN GRAPH-STRUCTURED NONLINEAR PROGRAMS
ArticleAbstract: We study solution sensitivity for nonlinear programs (NLPs) whose structures are induced by graphs.Palabras claves:GRAPHS, nonlinear programming, sensitivityAutores:Anitescu M., Shin S., Víctor M. ZavalaFuentes:scopus