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The Escalator Boxcar Train (EBT) is a tool widely used in the study of balance laws motivated by structure population dynamics. This paper proves that the approximate solutions defined through the EBT converge to exact solutions. Moreover, this method is rigorously shown to be effective also in computing optimal controls. As preliminary results, the well posedness of classes of PDEs and of ODEs comprising various biological models is also obtained. A specific application to welfare policies illustrates the whole procedure.
Colombo, Rinaldo M. 1 ; Gwiazda, Piotr 1 ; Rosińska, Magdalena 1
@article{COCV_2018__24_1_377_0, author = {Colombo, Rinaldo M. and Gwiazda, Piotr and Rosi\'nska, Magdalena}, title = {Optimization in structure population models through the {Escalator} {Boxcar} {Train}}, journal = {ESAIM: Control, Optimisation and Calculus of Variations}, pages = {377--399}, publisher = {EDP-Sciences}, volume = {24}, number = {1}, year = {2018}, doi = {10.1051/cocv/2017003}, mrnumber = {3843189}, zbl = {1407.65237}, language = {en}, url = {http://geodesic.mathdoc.fr/articles/10.1051/cocv/2017003/} }
TY - JOUR AU - Colombo, Rinaldo M. AU - Gwiazda, Piotr AU - Rosińska, Magdalena TI - Optimization in structure population models through the Escalator Boxcar Train JO - ESAIM: Control, Optimisation and Calculus of Variations PY - 2018 SP - 377 EP - 399 VL - 24 IS - 1 PB - EDP-Sciences UR - http://geodesic.mathdoc.fr/articles/10.1051/cocv/2017003/ DO - 10.1051/cocv/2017003 LA - en ID - COCV_2018__24_1_377_0 ER -
%0 Journal Article %A Colombo, Rinaldo M. %A Gwiazda, Piotr %A Rosińska, Magdalena %T Optimization in structure population models through the Escalator Boxcar Train %J ESAIM: Control, Optimisation and Calculus of Variations %D 2018 %P 377-399 %V 24 %N 1 %I EDP-Sciences %U http://geodesic.mathdoc.fr/articles/10.1051/cocv/2017003/ %R 10.1051/cocv/2017003 %G en %F COCV_2018__24_1_377_0
Colombo, Rinaldo M.; Gwiazda, Piotr; Rosińska, Magdalena. Optimization in structure population models through the Escalator Boxcar Train. ESAIM: Control, Optimisation and Calculus of Variations, Tome 24 (2018) no. 1, pp. 377-399. doi: 10.1051/cocv/2017003
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