Zobrazeno 1 - 5
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pro vyhledávání: '"Carlos Esteve-Yagüe"'
Autor:
Carlos Esteve-Yagüe, Enrique Zuazua
Publikováno v:
SIAM Journal on Mathematical Analysis. 54:5388-5423
We prove that the viscosity solution to a Hamilton-Jacobi equation with a smooth convex Hamiltonian of the form $H(x,p)$ is differentiable with respect to the initial condition. Moreover, the directional G��teaux derivatives can be explicitly com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::410aedb741e6d15b0cec303e80f9fc94
https://doi.org/10.1137/22m1469353
https://doi.org/10.1137/22m1469353
Publikováno v:
Nonlinearity
We present a new proof of the turnpike property for nonlinear optimal control problems, when the running target is a steady control-state pair of the underlying system. Our strategy combines the construction of quasi-turnpike controls via controllabi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6679df18e82d33f1533461933b1ca9b4
Reachable set for Hamilton-Jacobi equations with non-smooth Hamiltonian and scalar conservation laws
Autor:
Carlos Esteve-Yagüe, Enrique Zuazua
We give a full characterization of the range of the operator which associates, to any initial condition, the viscosity solution at time $T$ of a Hamilton-Jacobi equation with convex Hamiltonian. Our main motivation is to be able to treat the case of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d554827d6d3c08ae28686add19fd68a9
We consider the Selective Harmonic Modulation (SHM) problem, consisting in the design of a staircase control signal with some prescribed frequency components. In this work, we propose a novel methodology to address SHM as an optimal control problem i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b45070477e589be341fc75d74fa90e7
http://arxiv.org/abs/2103.10266
http://arxiv.org/abs/2103.10266
Autor:
Borjan Geshkovski, Carlos Esteve-Yagüe
We consider the neural ODE and optimal control perspective of supervised learning, with $\ell^1$-control penalties, where rather than only minimizing a final cost (the \emph{empirical risk}) for the state, we integrate this cost over the entire time
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3685d94d524c42de8148bbe37a45963
http://arxiv.org/abs/2102.13566
http://arxiv.org/abs/2102.13566