For a global field K and an elliptic curve Eη over K(T), Silverman's specialization theorem implies rank(Eη(K(T))) ≤ rank(Et(K)) for all but finitely many t ∈ P 1 (K). If this inequality is strict for all but finitely many t, the elliptic curve Eη is said to have elevated rank. All known examples of elevated rank for K = Q rest on the parity conjecture for elliptic curves over Q, and the exampl...

For a global field K and an elliptic curve Eη over K(T), Silverman's specialization theorem implies rank(Eη(K(T))) ≤ rank(Et(K)) for all but finitely many t ∈ P 1 (K). If this inequality is strict for all but finitely many t, the elliptic curve Eη is said to have elevated rank. All known examples of elevated rank for K = Q rest on the parity conjecture for elliptic curves over Q, and the exampl...

The adaptive identification of the impulse response of an innovation filter is considered. The impulse response is a finite sum of known basis functions with unknown coefficients. These unknown coefficients are estimated using a pseudolinear regression. This estimate is implemented using a square root algorithm based on a displacement rank structure. When the initial conditions have low displac...

This letter considers the problem of dictionary learning for sparse signal representation whose atoms have low mutual coherence. To learn such dictionaries, at each step, we first update the dictionary using the method of optimal directions (MOD) and then apply a dictionary rank shrinkage step to decrease its mutual coherence. In the rank shrinkage step, we first compute a rank 1 decomposition ...

The BFGS and DFP updates are perhaps the most successful Hessian and inverse Hessian approximations respectively for unconstrained minimization problems. This paper describes these methods in terms of two successive steps: rank reduction and rank restoration. From rank subtractivity and a powerful spectral result, the first step must necessarily result in a positive semidefinite matrix; and the...

The adaptive identiication of the impulse response of an innovation lter is considered. The impulse response is a nite sum of known basis functions with unknown coeecients. These unknown coeecients are estimated using a pseudolinear regression. This estimate is implemented using a square root algorithm based on a displacement rank structure. When the initial conditions have low displacement ran...

A survey of various link analysis and clustering algorithms such as Page Rank, Hyperlink-Induced Topic Search, Weighted Page Rank based on Visit of Links K-Means, Fuzzy K-Means. Ranking algorithms illustrated, Weighted Page Rank is more efficient than Hyperlink-induced Topic Search Whereas clustering algorithms has described Fuzzy Soft, Rough K-Means is a mixture of Rough K-Means and fuzzy soft...

Fix integers r, k, g with r > k > 0 and g ≥ 2. Let X be an integral projective curve with g := pa(X) and E a rank r torsion free sheaf on X which is a flat limit of a family of locally free sheaves on X. Here we prove the existence of a rank k subsheaf A of E such that r(deg(A)) ≥ k(deg(E)) − k(r − k)g. We show that for every g ≥ 9 there is an integral projective curve X,X not Gorenstein, and a...

In this work the dynamic descriptive complexity of the kclique query is studied in a framework introduced by Patnaik and Immerman. It is shown that when edges may only be inserted then k-clique can be maintained by a quantifier-free update program of arity k − 1, but it cannot be maintained by a quantifier-free update program of arity k−2 (even in the presence of unary auxiliary functions). Thi...

Rough set is a theory of data analysis and a mathematical tool for dealing with vagueness, incompleteness, and granularity. Matroid, as a branch of mathematics, is a structure that generalizes linear independence in vector spaces. In this paper, we establish the relationships between partition matroids and rough sets through k-rank matroids. On the one hand, k-rank matroids are proposed to repr...