We introduce a class of inverse problem estimators computed by mixing adaptively a family of linear estimators corresponding to different priors. Sparse mixing weights are calcula...
We derive a convex relaxation for cardinality constrained Principal Component Analysis (PCA) by using a simple representation of the L1 unit ball and standard Lagrangian duality. ...
Algorithms for the sparse matrix-vector multiplication (shortly SpM×V ) are important building blocks in solvers of sparse systems of linear equations. Due to matrix sparsity, the...
In this paper, we study the problem of L1-fitting a shape to a set of point, where the target is to minimize the sum of distances of the points to the shape, or alternatively the...
Abstract. We give processor-allocation algorithms for grid architectures, where the objective is to select processors from a set of available processors to minimize the average num...
Michael A. Bender, David P. Bunde, Erik D. Demaine...