The scalar sparse under-determined linear regression problem has had a rapid development with the multivariate version being of more recent interest. In this paper we pose a vector l₀ penalized multivariate regression problem to generate coefficient vectors with shared sparsity profile and then solve the problem with a new cyclic descent algorithm. We give optimality conditions and also discuss penalty parameter selection. Finally we present simulation results that compare our algorithm with alternatives.