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The GraphNet (aka S-Lasso), as well as other "sparsity + structure" priors like TV (Total-Variation), TV-L1, etc., are not easily applicable to brain data because of technical problems relating to the selection of the regularization parameters. Also, in their own right, such models lead to challenging high-dimensional optimization problems. In this manuscript, we present some heuristics for speeding up the overall optimization process: (a) Early-stopping, whereby one halts the optimizationdoi:10.1109/prni.2015.19 dblp:conf/prni/DohmatobETV15 fatcat:qwdmblz3nre7rcj2vvoplweqfi