Optimization and the Topology of Spaces of Parseval Frames

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Authors: Anthony Caine, Tom Needham, Clayton Shonkwiler
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Abstract

Abstract. A Parseval frame is a spanning set for a Hilbert space which satisfies the Parseval identity: a vector can be expressed as a linear combination of the frame whose coefficients are inner products with the frame vectors. There is considerable interest within the signal processing community in the structural properties of the space of finite-dimensional Parseval frames whose vectors all have the same norm or which satisfy more general prescribed norm constraints. In this paper, we introduce a function on the space of arbitrary lists of vectors that jointly measures the failure of the list of vectors to satisfy both the Parseval identity and given norm constraints. We show that, despite its nonconvexity, this function has no spurious local minimizers, thereby extending the Benedetto–Fickus theorem to this noncompact setting. In particular, this shows that the gradient descent converges to an equal norm Parseval frame when initialized within a dense open set in the associated matrix space. We then apply this result to study the topology of frame spaces. Using our Benedetto–Fickus-type result, we realize spaces of Parseval frames with prescribed norms as deformation retracts of simpler spaces, leading to explicit conditions which guarantee the vanishing of their homotopy groups. These conditions yield new path-connectedness results for spaces of real Parseval frames, generalizing the Frame Homotopy Theorem, which has seen significant interest in recent years. Reproducibility of computational results. This paper has been awarded the “SIAM Reproducibility Badge: Code and data available” as recognition that the authors have followed reproducibility principles valued by SIMAX and the scientific computing community. There is no code or data associated with this paper, since it is a theory paper without numerical experiments. [Formula: see text]

Fields of Study & Subjects

Advanced Numerical Analysis Techniques

Publication details

Journal / source
SIAM Journal on Matrix Analysis and Applications
Volume
47
Issue
3
Pages
1375–1399
Publication date
22 July 2026
Language
EN