Neural networks for computing eigenvalues and eigenvectors
Copyright policy )Neural networks for computing eigenvalues and eigenvectors
The authors consider the problem of computing an eigendecomposition of a square matrix. They formulate the problem as a constrained optimization problem and construct a penalty function to be minimized. They solve the resulting unconstrained optimization problem by designing neural networks and applying a back-propagation learning scheme, which is similar to the steepest descent algorithm in numerical optimization parlance. The result of numerical simulations on some small test problems are presented.
- University of Erlangen-Nuremberg Germany
- Warsaw University of Technology Poland
Numerical computation of eigenvalues and eigenvectors of matrices, eigendecomposition, eigenvalues, eigenvectors, unconstrained optimization, test problems, neural networks, steepest descent algorithm
Numerical computation of eigenvalues and eigenvectors of matrices, eigendecomposition, eigenvalues, eigenvectors, unconstrained optimization, test problems, neural networks, steepest descent algorithm
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