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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/"><rdf:Description rdf:about="https://repozitorij.uni-lj.si/IzpisGradiva.php?id=188108"><dc:title>Stability and complexity of global iterative solvers for the Kadanoff-Baym equations</dc:title><dc:creator>Gašperlin,	Jože	(Avtor)
	</dc:creator><dc:creator>Golež,	Denis	(Avtor)
	</dc:creator><dc:creator>Kaye,	Jason	(Avtor)
	</dc:creator><dc:subject>Anderson model</dc:subject><dc:subject>Green's functions</dc:subject><dc:subject>Mott insulators</dc:subject><dc:description>Although the Kadanoff-Baym equations are typically solved using time-stepping methods, iterative global-in-time solvers offer potential algorithmic advantages, particularly when combined with compressed representations of two-time objects. We examine the computational complexity and stability of several global-in-time iterative methods, including multiple variants of fixed point iteration, Jacobian-free methods, and a Newton-Krylov method using automatic differentiation. We consider the ramped and periodically-driven Falicov-Kimball and Hubbard models within time-dependent dynamical mean-field theory. Although we observe that several iterative methods yield stable convergence at large propagation times, a standard forward fixed point iteration does not. We find that the number of iterations required to converge to a given accuracy with a fixed time step size scales roughly linearly with the number of time steps. This scaling is associated with the formation of a propagating front in the residual error, whose velocity is method-dependent. We identify key challenges which must be addressed in order to make global solvers competitive with time-stepping methods.</dc:description><dc:date>2026</dc:date><dc:date>2026-09-18 10:03:06</dc:date><dc:type>Članek v reviji</dc:type><dc:identifier>188108</dc:identifier><dc:language>sl</dc:language></rdf:Description></rdf:RDF>
