
Smoothedadaptive perturbed inverse iteration for elliptic eigenvalue problems
We present a perturbed subspace iteration algorithm to approximate the l...
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Hybrid Projection Methods with Recycling for Inverse Problems
Iterative hybrid projection methods have proven to be very effective for...
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A direct method for solving inverse SturmLiouville problems
We consider two main inverse SturmLiouville problems: the problem of re...
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Efficient and Accurate Algorithms for Solving the BetheSalpeter Eigenvalue Problem for Crystalline Systems
Optical properties of materials related to light absorption and scatteri...
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A study on reliability of a koutofn system equipped with a cold standby component based on copula
A koutofn system consisting of n exchangeable components equipped wit...
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Inverse Kinematics of Some General 6R/P Manipulators
We develop an algorithm that solves the inverse kinematics of general se...
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Eigenvalue embedding of undamped piezoelectric structure system with nospillover
In this paper, the eigenvalue embedding problem of the undamped piezoele...
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Linearizability of eigenvector nonlinearities
We present a method to linearize, without approximation, a specific class of eigenvalue problems with eigenvector nonlinearities (NEPv), where the nonlinearities are expressed by scalar functions that are defined by a quotient of linear functions of the eigenvector. The exact linearization relies on an equivalent multiparameter problem (MEP) that contains the exact solutions of the NEPv. Due to the characterization of MEPs in terms of a generalized eigenvalue problem this provides a direct way to compute all NEPv solutions for small problems, and it opens up the possibility to develop locally convergent iterative methods for larger problems. Moreover, the linear formulation allows us to easily determine the number of solutions of the NEPv. We propose two numerical schemes that exploit the structure of the linearization: inverse iteration and residual inverse iteration. We show how symmetry in the MEP can be used to improve reliability and reduce computational cost of both methods. Two numerical examples verify the theoretical results and a third example shows the potential of a hybrid scheme that is based on a combination of the two proposed methods.
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