NA
2011
13 years 7 months ago
2011
We are interested in computing the Fermi-Dirac matrix function in which the matrix argument is the Hamiltonian matrix arising from Density Function Theory (DFT) applications. More...
MP
2011
13 years 7 months ago
2011
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in machine learning, control theory, and discrete geometry. This c...
MCS
2011
Springer
13 years 7 months ago
2011
Springer
Consider a matrix valued function A(x) ∈ Rm×n , m ≥ n, smoothly depending on parameters x ∈ Ω ⊂ R2 , where Ω is simply connected and bounded. We consider a technique t...
FOCM
2011
13 years 7 months ago
2011
The matrix rank minimization problem has applications in many fields such as system identification, optimal control, low-dimensional embedding etc. As this problem is NP-hard in ...
CORR
2011
Springer
13 years 7 months ago
2011
Springer
Abstract—We consider a certain class of large random matrices, composed of independent column vectors with zero mean and different covariance matrices, and derive asymptotically ...
CORR
2011
Springer
13 years 7 months ago
2011
Springer
Wigderson and Xiao presented an efficient derandomization of the matrix Chernoff bound using the method of pessimistic estimators [WX08]. Building on their construction, we prese...
CORR
2011
Springer
13 years 7 months ago
2011
Springer
Circulant matrices play a central role in a recently proposed formulation of three-way data computations. In this setting, a three-way table corresponds to a matrix where each “...
CDC
2010
IEEE
13 years 7 months ago
2010
IEEE
Given linear matrix inequalities (LMIs) L1 and L2 in the same number of variables it is natural to ask: (Q1) does one dominate the other, that is, does L1(X) 0 imply L2(X) 0? (Q2) ...
CVPR
2011
IEEE
13 years 7 months ago
2011
IEEE
Exact recovery from contaminated visual data plays an important role in various tasks. By assuming the observed data matrix as the addition of a low-rank matrix and a sparse matri...
CVPR
2011
IEEE
13 years 7 months ago
2011
IEEE
We consider the problem of fitting one or more subspaces to a collection of data points drawn from the subspaces and corrupted by noise/outliers. We pose this problem as a rank m...