Nanglik, V.P.; (1970)On the construction of systems and designs useful in the theory of random search."
... criteria for comparing search systems is given together with some methods for constructing systems optimal in this sense. ...
... criteria for comparing search systems is given together with some methods for constructing systems optimal in this sense. ...
ON BEST APPROXIMATIONS OF POLYNOMIALS IN
... The problem (1.4) is equal to (1.3) with b(A) = Am+1 . Because of its relation to the convergence of the Arnoldi method [1] for approximating the eigenvalues of A, the uniquely defined monic polynomial z m+1 ā pā that solves (1.4) is called the (m + 1)st ideal Arnoldi polynomial of A. In a paper tha ...
... The problem (1.4) is equal to (1.3) with b(A) = Am+1 . Because of its relation to the convergence of the Arnoldi method [1] for approximating the eigenvalues of A, the uniquely defined monic polynomial z m+1 ā pā that solves (1.4) is called the (m + 1)st ideal Arnoldi polynomial of A. In a paper tha ...
Chapter_10_Linear EquationsQ
... A an n-square coefficient matrix, X column vector of n variables, H non-zero column vector of n components. (i) List down all the ways you can think of that can be employed to solve the linear equation systems. I can think of 5, how many can you think of? (ii) As an exercise, solve the given equatio ...
... A an n-square coefficient matrix, X column vector of n variables, H non-zero column vector of n components. (i) List down all the ways you can think of that can be employed to solve the linear equation systems. I can think of 5, how many can you think of? (ii) As an exercise, solve the given equatio ...
An Algorithm for Solving Scaled Total Least Squares Problems
... hand side vectors b. In Section 2, we first describe a complete orthogonal decomposition (COD) [2] to illustrate the ideas behind our algorithm. Then we present a practical algorithm for solving the STLS problem using the rank revealing ULV decomposition (RRULVD) [7]. The the computation of the RRUL ...
... hand side vectors b. In Section 2, we first describe a complete orthogonal decomposition (COD) [2] to illustrate the ideas behind our algorithm. Then we present a practical algorithm for solving the STLS problem using the rank revealing ULV decomposition (RRULVD) [7]. The the computation of the RRUL ...
REDUCING THE ADJACENCY MATRIX OF A TREE
... rank(A) 2 1 (T ): Let S E be any set of 1(T ) disjoint edges, and let X be the set of vertices incident with the edges in S . By the disjointness of S , jX j = 2 1. Let A0 = A[X jX ] be the principal submatrix of A whose rows and columns correspond to X . To establish (4), it suces to show ...
... rank(A) 2 1 (T ): Let S E be any set of 1(T ) disjoint edges, and let X be the set of vertices incident with the edges in S . By the disjointness of S , jX j = 2 1. Let A0 = A[X jX ] be the principal submatrix of A whose rows and columns correspond to X . To establish (4), it suces to show ...
Non-negative matrix factorization
NMF redirects here. For the bridge convention, see new minor forcing.Non-negative matrix factorization (NMF), also non-negative matrix approximation is a group of algorithms in multivariate analysis and linear algebra where a matrix V is factorized into (usually) two matrices W and H, with the property that all three matrices have no negative elements. This non-negativity makes the resulting matrices easier to inspect. Also, in applications such as processing of audio spectrograms non-negativity is inherent to the data being considered. Since the problem is not exactly solvable in general, it is commonly approximated numerically.NMF finds applications in such fields as computer vision, document clustering, chemometrics, audio signal processing and recommender systems.