Principal component analysis (PCA) is a method of choice for dimension reduction. In the current context of data explosion, online techniques that do not require storing all data in memory are ...

ABSTRACT: We will study the generalized Steklov-Robin eigenproblem (with possibly matrix weights) in which the spectral parameter is both in the system and on the boundary. The weights may be singular ...

I'm attempting to use Ray to scale up a parameter sweep which involves solving an eigenvalue problem over a large parameter space. The matrices are typically 200 x 200, and I run roughly on 300+ CPU ...

In the seismic design and analysis of important structures, ground motion time histories are generally required as the input for the conduction of seismic response history analysis. Taking a selected ...

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We present in this paper a new method for solving polynomial eigenvalue problem. We give methods that decompose a skew-Hamiltonian matrix using Cholesky like-decomposition. We transform first the ...

As can be deduced from the depicted output, the first execution, which employs the guess, fails, whereas the second (employing a random guess) succeeds. λref are the eigenvalues on the full matrix ...

Abstract: This paper describes a parameter estimation algorithm applicable for a model structure in the form of an overdetermined polynomial eigenproblem. An example of a third-order synchronous ...