We consider the problem of computing low-rank approximations of matrices. The novel aspects of our approach are that we require the low-rank approximations be written in a factorized form with sparse factors and the degree of sparsity of the factors can be traded oo for reduced reconstruction error by certain user determined parameters. We give a detailed error analysis of our proposed algorith...

A framework for the probabilistic finite element model updating based on measured modal data is presented. The described applied to a seven-storey building made of cross-laminated timber panels. experimental estimates forced vibration test are used in process updating. First, generalized Polynomial Chaos surrogate derived representing map from parameters eigenfrequencies and eigenvectors. To ov...

We show that, if k and ` are positive integers and r is sufficiently large, then the number of rank-k flats in a rank-r matroid M with no U2,`+2-minor is less than or equal to number of rank-k flats in a rank-r projective geometry over GF(q), where q is the largest prime power not exceeding `.

We give algorithms for approximation by low-rank positive semidefinite (PSD) matrices. For symmetric input matrix A ∈ Rn×n, target rank k, and error parameter ε > 0, one algorithm finds with constant probability a PSD matrix Ỹ of rank k such that ‖A− Ỹ ‖2F ≤ (1+ε)‖A−Ak,+‖ 2 F , where Ak,+ denotes the best rank-k PSD approximation to A, and the norm is Frobenius. The algorithm takes time O(nnz(A...

We investigate, theoretically and empirically, the effectiveness of kernel K-means++ samples as landmarks in the Nyström method for low-rank approximation of kernel matrices. Previous empirical studies (Zhang et al., 2008; Kumar et al., 2012) observe that the landmarks obtained using (kernel) K-means clustering define a good lowrank approximation of kernel matrices. However, the existing work d...

Fixed-parameter algorithms and kernelization are two powerful methods to solve NP-hard problems. Yet, so far those algorithms have been largely restricted to static inputs. In this paper we provide fixed-parameter algorithms and kernelizations for fundamental NPhard problems with dynamic inputs. We consider a variety of parameterized graph and hitting set problems which are known to have f(k)n ...

In this paper it is shown that if T ∈ L(H) satisfies (i) T is a pure hyponormal operator; (ii) [T ∗, T ] is of rank-two; and (iii) ker [T ∗, T ] is invariant for T , then T is either a subnormal operator or the Putinar’s matricial model of rank two. More precisely, if T |ker [T∗,T ] has the rank-one self-commutator then T is subnormal and if instead T |ker [T∗,T ] has the ranktwo self-commutato...

We answer a question due to A. Myasnikov by proving that all expected ranks occur as the ranks of intersections of finitely generated subgroups of free groups. Mathematics Subject Classification (2000): 20E05 Let F be a free group. Let H and K be nontrivial finitely generated subgroups of F . It is a theorem of Howson [1] that H ∩K has finite rank. H. Neumann proved in [2] that rank(H ∩K)− 1 ≤ ...