On the Convergence of Block Majorization-Minimization Algorithms on the Grassmann Manifold

The Majorization-Minimization (MM) framework is widely used to derive efficient algorithms for specific problems that require the optimization of a cost function (which can be convex or not). It is based on a sequential optimization of a surrogate function over closed convex sets. A natural extensio...

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Veröffentlicht in:	IEEE signal processing letters Jg. 31; S. 1314 - 1318
Hauptverfasser:	Lopez, Carlos Alejandro, Riba, Jaume
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Constraints Convergence Convexity Cost function geodesically convex optimization Grassmann manifold Independent variables majorization-minimization Manifolds Manifolds (mathematics) Minimization Non-convex optimization Optimization Principal component analysis Riemannian optimization Signal processing Signal processing algorithms
ISSN:	1070-9908, 1558-2361
Online-Zugang:	Volltext
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