This is the first of a series of papers devoted to a thorough analysis of the class of gradient flows in a metric space (X,d) that can be characterized by Evolution Variational Inequalities (EVI). We present new results concerning the structural properties of solutions to the EVI formulation, such as contraction, regularity, asymptotic expansion, precise energy identity, stability, asymptotic behavior and their link with the geodesic convexity of the driving functional. Under the crucial assumption of the existence of an EVI gradient flow, we will also prove two main results: – the equivalence with the De Giorgi variational characterization of curves of maximal slope; – the convergence of the Minimizing Movement-JKO scheme to the EVI gradient flow, with an explicit and uniform error estimate of order 1/2 with respect to the step size, independent of any geometric hypothesis (as upper or lower curvature bounds) on d. In order to avoid any compactness assumption, we will also introduce a suitable relaxation of the Minimizing Movement algorithm obtained by the Ekeland variational principle, and we will prove its uniform convergence as well.
Gradient flows and Evolution Variational Inequalities in metric spaces. I: Structural properties
Muratori M.;Savare G.
2020-01-01
Abstract
This is the first of a series of papers devoted to a thorough analysis of the class of gradient flows in a metric space (X,d) that can be characterized by Evolution Variational Inequalities (EVI). We present new results concerning the structural properties of solutions to the EVI formulation, such as contraction, regularity, asymptotic expansion, precise energy identity, stability, asymptotic behavior and their link with the geodesic convexity of the driving functional. Under the crucial assumption of the existence of an EVI gradient flow, we will also prove two main results: – the equivalence with the De Giorgi variational characterization of curves of maximal slope; – the convergence of the Minimizing Movement-JKO scheme to the EVI gradient flow, with an explicit and uniform error estimate of order 1/2 with respect to the step size, independent of any geometric hypothesis (as upper or lower curvature bounds) on d. In order to avoid any compactness assumption, we will also introduce a suitable relaxation of the Minimizing Movement algorithm obtained by the Ekeland variational principle, and we will prove its uniform convergence as well.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
