@ARTICLE{sael13,
  author = {B. Savas and L. Eld{\' e}n},
  title = {Krylov-type methods for tensor computations I},
  journal = {Linear Algebra and its Applications},
  year = {2013},
  volume = {438},
  pages = {891-918},
  abstract = {Several Krylov-type procedures are introduced that generalize
	matrix Krylov methods for tensor computations. They are denoted
	minimal Krylov recursion, maximal Krylov recursion, and contracted
	tensor product Krylov recursion. It is proved that, for a given tensor
	A with multilinear rank-(p, q, r), the minimal Krylov recursion extracts
	the correct subspaces associated to the tensor in p+q+r number
	of tensor-vector-vector multiplications. An optimized minimal
	Krylov procedure is described that, for a given multilinear rank of
	an approximation, produces a better approximation than the standard
	minimal recursion. We further generalize the matrix Krylov
	decomposition to a tensor Krylov decomposition. The tensor Krylov
	methods are intended for the computation of low multilinear rank
	approximations of large and sparse tensors, but they are also useful
	for certain dense and structured tensors for computing their higher
	order singular value decompositions or obtaining starting points for
	the best low-rank computations of tensors. A set of numerical experiments,
	using real-world and synthetic data sets, illustrate some
	of the properties of the tensor Krylov methods.},
  doi = {http://dx.doi.org/10.1016/j.laa.2011.12.007},
  issn = {0024-3795},
  keywords = {Tensor},
  url = {http://www.sciencedirect.com/science/article/pii/S0024379511007877}
}