Geodesic Convexity of the Symmetric Eigenvalue Problem and Convergence of Steepest Descent

Foivos Alimisis () and Bart Vandereycken
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Foivos Alimisis: University of Geneva
Bart Vandereycken: University of Geneva

Journal of Optimization Theory and Applications, 2024, vol. 203, issue 1, No 32, 920-959

Abstract: Abstract We study the convergence of the Riemannian steepest descent algorithm on the Grassmann manifold for minimizing the block version of the Rayleigh quotient of a symmetric matrix. Even though this problem is non-convex in the Euclidean sense and only very locally convex in the Riemannian sense, we discover a structure for this problem that is similar to geodesic strong convexity, namely, weak-strong convexity. This allows us to apply similar arguments from convex optimization when studying the convergence of the steepest descent algorithm but with initialization conditions that do not depend on the eigengap $$\delta $$ δ . When $$\delta >0$$ δ > 0 , we prove exponential convergence rates, while otherwise the convergence is algebraic. Additionally, we prove that this problem is geodesically convex in a neighbourhood of the global minimizer of radius $${\mathcal {O}}(\sqrt{\delta })$$ O ( δ ) .

Keywords: Block Rayleigh quotient; Grassmann manifold; Geodesic convexity; Riemannian optimization; Low-rank approximation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10957-024-02538-8

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