A Subspace Method for Large-Scale Eigenvalue Optimization

DOI10.1137/16M1070025zbMath1378.65090arXiv1508.04214MaRDI QIDQ3130422

Fatih Kangal, Emre Mengi, Karl Meerbergen, Wim Michiels

Publication date: 22 January 2018

Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1508.04214

zbMATH Keywords

eigenvalue optimizationorthogonal projectionmatrix-valued functionlarge scaleeigenvalue perturbation theoryparameter dependent compact operator

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Nonconvex programming, global optimization (90C26) Linear operators defined by compactness properties (47B07) Linear operators on special spaces (weighted shifts, operators on sequence spaces, etc.) (47B37)

Related Items (12)

Generalized derivatives of eigenvalues of a symmetric matrix ⋮ Subspace Acceleration for the Crawford Number and Related Eigenvalue Optimization Problems ⋮ Subspace method for the estimation of large-scale structured real stability radius ⋮ Model Order Reduction in Contour Integral Methods for Parametric PDEs ⋮ Large-Scale Computation of $\mathcal{L}_\infty$-Norms by a Greedy Subspace Method ⋮ Low-Rank Matrix Iteration Using Polynomial-Filtered Subspace Extraction ⋮ Nonlinear Eigenvector Methods for Convex Minimization over the Numerical Range ⋮ Large-Scale and Global Maximization of the Distance to Instability ⋮ Polynomial (chaos) approximation of maximum eigenvalue functions. Efficiency and limitations ⋮ Inheritance properties of Krylov subspace methods for continuous-time algebraic Riccati equations ⋮ Subspace Acceleration for Large-Scale Parameter-Dependent Hermitian Eigenproblems ⋮ A Subspace Framework for ${\mathcal H}_\infty$-Norm Minimization

Uses Software

• MatrixMarket

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