New Power Method for Solving Eigenvalue Problems
- URL: http://arxiv.org/abs/2211.06303v2
- Date: Mon, 07 Oct 2024 08:51:59 GMT
- Title: New Power Method for Solving Eigenvalue Problems
- Authors: I Wayan Sudiarta, Hadi Susanto,
- Abstract summary: We present a new power method to obtain solutions of eigenvalue problems.
The method can determine not only the dominant or lowest eigenvalues but also all eigenvalues without the need for a deflation procedure.
- Score: 0.0
- License:
- Abstract: We present a new power method to obtain solutions of eigenvalue problems. The method can determine not only the dominant or lowest eigenvalues but also all eigenvalues without the need for a deflation procedure. The method uses a functional of an operator (or a matrix) to select or filter an eigenvalue. The method can freely select a solution by varying a parameter associated to an estimate of the eigenvalue. The convergence of the method is highly dependent on how closely the parameter to the eigenvalues. In this paper, numerical results of the method are shown to be in excellent agreement with the analytical ones.
Related papers
- Just another conditionally-solvable non-relativistic quantum-mechanical model [0.0]
We show that a perturbed Coulomb problem discussed recently is conditionally solvable.
We obtain the exact eigenvalues and eigenfunctions and compare the former with eigenvalues calculated by means of a numerical method.
arXiv Detail & Related papers (2024-09-30T18:19:32Z) - Annealing-based approach to solving partial differential equations [0.0]
The proposed algorithm allows the computation of eigenvectors at arbitrary precision without increasing the number of variables using an Ising machine.
Simple examples solved using this method and theoretical analysis provide a guideline for appropriate parameter settings.
arXiv Detail & Related papers (2024-06-25T08:30:00Z) - Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction [55.57072563835959]
spectral graph neural networks are characterized by filters.
We propose an eigenvalue correction strategy that can free filters from the constraints of repeated eigenvalue inputs.
arXiv Detail & Related papers (2024-01-28T08:12:00Z) - Using Variational Eigensolvers on Low-End Hardware to Find the Ground
State Energy of Simple Molecules [0.0]
Key properties of physical systems can be described by the eigenvalues of matrices that represent the system.
Computational algorithms that determine the eigenvalues of these matrices exist, but they generally suffer from a loss of performance as the matrix grows in size.
This process can be expanded to quantum computation to find the eigenvalues with better performance than the classical algorithms.
arXiv Detail & Related papers (2023-10-29T18:36:18Z) - On the Effectiveness of Parameter-Efficient Fine-Tuning [79.6302606855302]
Currently, many research works propose to only fine-tune a small portion of the parameters while keeping most of the parameters shared across different tasks.
We show that all of the methods are actually sparse fine-tuned models and conduct a novel theoretical analysis of them.
Despite the effectiveness of sparsity grounded by our theory, it still remains an open problem of how to choose the tunable parameters.
arXiv Detail & Related papers (2022-11-28T17:41:48Z) - Neural Networks Based on Power Method and Inverse Power Method for
Solving Linear Eigenvalue Problems [4.3209899858935366]
We propose two kinds of neural networks inspired by power method and inverse power method to solve linear eigenvalue problems.
The eigenfunction of the eigenvalue problem is learned by the neural network.
We show that accurate eigenvalue and eigenfunction approximations can be obtained by our methods.
arXiv Detail & Related papers (2022-09-22T16:22:11Z) - Solving Constrained Variational Inequalities via an Interior Point
Method [88.39091990656107]
We develop an interior-point approach to solve constrained variational inequality (cVI) problems.
We provide convergence guarantees for ACVI in two general classes of problems.
Unlike previous work in this setting, ACVI provides a means to solve cVIs when the constraints are nontrivial.
arXiv Detail & Related papers (2022-06-21T17:55:13Z) - Automated differential equation solver based on the parametric
approximation optimization [77.34726150561087]
The article presents a method that uses an optimization algorithm to obtain a solution using the parameterized approximation.
It allows solving the wide class of equations in an automated manner without the algorithm's parameters change.
arXiv Detail & Related papers (2022-05-11T10:06:47Z) - Gross misinterpretation of a conditionally solvable eigenvalue equation [0.0]
We solve an eigenvalue equation that appears in several papers about a wide range of physical problems.
We compare the resulting eigenvalues with those provided by the truncation condition.
In this way we prove that those physical predictions are merely artifacts of the truncation condition.
arXiv Detail & Related papers (2020-11-12T15:08:11Z) - On the Adversarial Robustness of LASSO Based Feature Selection [72.54211869067979]
In the considered model, there is a malicious adversary who can observe the whole dataset, and then will carefully modify the response values or the feature matrix.
We formulate the modification strategy of the adversary as a bi-level optimization problem.
Numerical examples with synthetic and real data illustrate that our method is efficient and effective.
arXiv Detail & Related papers (2020-10-20T05:51:26Z) - Eigendecomposition-Free Training of Deep Networks for Linear
Least-Square Problems [107.3868459697569]
We introduce an eigendecomposition-free approach to training a deep network.
We show that our approach is much more robust than explicit differentiation of the eigendecomposition.
Our method has better convergence properties and yields state-of-the-art results.
arXiv Detail & Related papers (2020-04-15T04:29:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.