Frengression is a deep generative realization of the frugal parameterization that models the joint distribution of covariates, treatments, and outcomes and allows direct sampling from user-specified interventional distributions.
Newton-Krylov solvers for time-steppers
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abstract
We study how the Newton-GMRES iteration can enable dynamic simulators (time-steppers) to perform fixed-point and path-following computations.For a class of dissipative problems, whose dynamics are characterized by a slow manifold, the Jacobian matrices in such computations are compact perturbations of the identity. We examine the number of GMRES iterations required for each nonlinear iteration as a function of the dimension of the slow subspace and the time-stepper reporting horizon. In a path-following computation, only a small number (one or two) of additional GMRES iterations is required.
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Frugal, Flexible, Faithful: Causal Data Simulation via Frengression
Frengression is a deep generative realization of the frugal parameterization that models the joint distribution of covariates, treatments, and outcomes and allows direct sampling from user-specified interventional distributions.