نتایج جستجو برای: eigenvectors and gram

تعداد نتایج: 16834237  

Journal: :Numerical Linear Algebra With Applications 2021

Solving linear systems is often the computational bottleneck in real-life problems. Iterative solvers are only option due to complexity of direct algorithms or because system matrix not explicitly known. Here, we develop a two-level preconditioner for regularized least squares involving feature data matrix. Variants this may appear machine learning applications, such as ridge regression, logist...

2004
Nicola Mastronardi Marc Van Barel Ellen Van Camp Raf Vandebril N. Mastronardi M. Van Barel R. Vandebril

A real symmetric matrix of order n has a full set of orthogonal eigenvectors. The most used approach to compute the spectrum of such matrices reduces first the dense symmetric matrix into a symmetric structured one, i.e., tridiagonal matrices or semiseparable matrices. This step is accomplished in O(n 3) operations. Once the latter symmetric structured matrix is available, its spectrum is compu...

Journal: :Acta biochimica Polonica 2016
Carlos Polanco

Antibacterial peptides are subject to broad research due to their potential application and the benefit they can provide for a wide range of diseases. In this work, a mathematical-computational method, called the Polarity Vector Method, is introduced that has a high discriminative level (>70%) to identify peptides associated with Gram (-) bacteria, Gram (+) bacteria, cancer cells, fungi, insect...

2012
Masami Takata Hiroyuki Ishigami Kinji Kimura Yoshimasa Nakamura

In this paper, we compare with the inverse iteration algorithms on PowerXCell 8i processor, which has been known as a heterogeneous environment. When some of all the eigenvalues are close together or there are clusters of eigenvalues, reorthogonalization must be adopted to all the eigenvectors associated with such eigenvalues. Reorthogonalization algorithms need a lot of computational cost. The...

2011
Jeong-Min Yun Seungjin Choi

Kernel principal component analysis (KPCA) is a widelyused statistical method for representation learning, where PCA is performed in reproducing kernel Hilbert space (RKHS) to extract nonlinear features from a set of training examples. Despite the success in various applications including face recognition, KPCA does not scale up well with the sample size, since, as in other kernel methods, it i...

Journal: :CoRR 2017
Vadim Zaliva

The problem of constructing an orthogonal set of eigenvectors for a DFT matrix is well studied. An elegant solution is mentioned by Matveev in [1]. In this paper, we present a distilled form of his solution including some steps unexplained in his paper, along with correction of typos and errors using more consistent notation. Then we compare the computational complexity of his method with the m...

پایان نامه :وزارت علوم، تحقیقات و فناوری - دانشگاه صنعتی اصفهان - دانشکده ریاضی 1389

one of the most important number sequences in mathematics is fibonacci sequence. fibonacci sequence except for mathematics is applied to other branches of science such as physics and arts. in fact, between anesthetics and this sequence there exists a wonderful relation. fibonacci sequence has an importance characteristic which is the golden number. in this thesis, the golden number is observed ...

Journal: :Linear Algebra and its Applications 1998

H. Myrnouri

We study certain function algebras and their operator algebra completions on r-discrete abelian groupoids, the corresponding conditional expectations, maximal abelian subalgebras (masa) and eigen-functionals. We give a semidirect product decomposition for an abelian groupoid. This is done through a matched pair and leads to a C*-diagonal (for a special case). We use this decomposition to study ...

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