A Method for Auto-Differentiation of the Voronoi Tessellation
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arXiv
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| Format: | Preprint |
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2023
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| author | Shumilin, Sergei Ryabov, Alexander Barannikov, Serguei Burnaev, Evgeny Vanovskii, Vladimir |
| author_facet | Shumilin, Sergei Ryabov, Alexander Barannikov, Serguei Burnaev, Evgeny Vanovskii, Vladimir |
| contents | Voronoi tessellation, also known as Voronoi diagram, is an important computational geometry technique that has applications in various scientific disciplines. It involves dividing a given space into regions based on the proximity to a set of points. Autodifferentiation is a powerful tool for solving optimization tasks. Autodifferentiation assumes constructing a computational graph that allows to compute gradients using backpropagation algorithm. However, often the Voronoi tessellation remains the only non-differentiable part of a pipeline, prohibiting end-to-end differentiation. We present the method for autodifferentiation of the 2D Voronoi tessellation. The method allows one to construct the Voronoi tessellation and pass gradients, making the construction end-to-end differentiable. We provide the implementation details and present several important applications. To the best of our knowledge this is the first autodifferentiable realization of the Voronoi tessellation providing full set of Voronoi geometrical parameters in a differentiable way. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_16192 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Method for Auto-Differentiation of the Voronoi Tessellation Shumilin, Sergei Ryabov, Alexander Barannikov, Serguei Burnaev, Evgeny Vanovskii, Vladimir Computational Geometry Machine Learning Voronoi tessellation, also known as Voronoi diagram, is an important computational geometry technique that has applications in various scientific disciplines. It involves dividing a given space into regions based on the proximity to a set of points. Autodifferentiation is a powerful tool for solving optimization tasks. Autodifferentiation assumes constructing a computational graph that allows to compute gradients using backpropagation algorithm. However, often the Voronoi tessellation remains the only non-differentiable part of a pipeline, prohibiting end-to-end differentiation. We present the method for autodifferentiation of the 2D Voronoi tessellation. The method allows one to construct the Voronoi tessellation and pass gradients, making the construction end-to-end differentiable. We provide the implementation details and present several important applications. To the best of our knowledge this is the first autodifferentiable realization of the Voronoi tessellation providing full set of Voronoi geometrical parameters in a differentiable way. |
| title | A Method for Auto-Differentiation of the Voronoi Tessellation |
| topic | Computational Geometry Machine Learning |
| url | https://arxiv.org/abs/2312.16192 |