<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giang Tran</style></author><author><style face="normal" font="default" size="100%">Hayden Schaeffer</style></author><author><style face="normal" font="default" size="100%">William Feldman</style></author><author><style face="normal" font="default" size="100%">Stanley Osher</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An L1 Penalty Method for General Obstacle Problems</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM Journal on Applied Mathematics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">75</style></volume><pages><style face="normal" font="default" size="100%">1424-1444</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align:justify&quot;&gt;
	We construct an efficient numerical scheme for solving obstacle problems in divergence form. The numerical method is based on a reformulation of the obstacle in terms of an $L^1$-like penalty on the variational problem. The reformulation is an exact regularizer in the sense that for a large (but finite) penalty parameter, we recover the exact solution. Our formulation is applied to classical elliptic obstacle problems as well as some related free boundary problems, for example, the two-phase membrane problem and the Hele--Shaw model. One advantage of the proposed method is that the free boundary inherent in the obstacle problem arises naturally in our energy minimization without any need for problem specific or complicated discretization. Also, our scheme also works for nonlinear variational inequalities arising from convex minimization problems.
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