Towards Scalable Persistence-Based Topological Optimization

• URL: http://arxiv.org/abs/2605.10996v1
• Date: Sat, 09 May 2026 15:47:20 GMT
• Title: Towards Scalable Persistence-Based Topological Optimization
• Abstract summary: Persistence-based topological optimization deforms a point cloud $X subset mathbbRd$ by minimizing objectives of the form $L(X) = ell(mathrmDgm(X))$, where $mathrmDgm(X)$ is a persistence diagram.<n>In practice, optimization is limited by two coupled issues: persistent homology is typically computed on subsamples, and the resulting topological gradients are highly sparse, with only a few anchor points receiving nonzero updates.
• Score: 44.16669776030478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Persistence-based topological optimization deforms a point cloud $X \subset \mathbb{R}^d$ by minimizing objectives of the form $L(X) = \ell(\mathrm{Dgm}(X))$, where $\mathrm{Dgm}(X)$ is a persistence diagram. In practice, optimization is limited by two coupled issues: persistent homology is typically computed on subsamples, and the resulting topological gradients are highly sparse, with only a few anchor points receiving nonzero updates. Motivated by diffeomorphic interpolation, which extends sparse gradients to smooth ambient vector fields via Reproducing Kernel Hilbert Space (RKHS) interpolation, we propose a more scalable pipeline that improves both subsampling and gradient extension. We introduce subsampling via random slicing, a lightweight scheme that promotes iteration-wise geometric coverage and mitigates density bias. We further replace the costly kernel solve with a fast Nadaraya-Watson (NW) Gaussian convolution, producing a globally defined smooth update field at a fraction of the computational cost, while being more suited for topological optimization tasks. We provide theoretical guarantees for NW smoothing, including anchor approximation bounds and global Lipschitz estimates. Experiments in $2$D and $3$D show that combining random slicing with NW smoothing yields consistent speedups and improved objective values over other baselines on common persistence losses.

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