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Main Authors: Fan, Jun, Shan, Xiaoya, Xiu, Xianchao
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2502.02280
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author Fan, Jun
Shan, Xiaoya
Xiu, Xianchao
author_facet Fan, Jun
Shan, Xiaoya
Xiu, Xianchao
contents In this paper, we propose a fast and convergent algorithm to solve unassigned distance geometry problems (uDGP). Technically, we construct a novel quadratic measurement model by leveraging $\ell_0$-norm instead of $\ell_1$-norm in the literature. To solve the nonconvex model, we establish its optimality conditions and develop a fast iterative hard thresholding (IHT) algorithm. Theoretically, we rigorously prove that the whole generated sequence converges to the L-stationary point with the help of the Kurdyka-Lojasiewicz (KL) property. Numerical studies on the turnpike and beltway problems validate its superiority over existing $\ell_1$-norm-based method.
format Preprint
id arxiv_https___arxiv_org_abs_2502_02280
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Fast and Convergent Algorithm for Unassigned Distance Geometry Problems
Fan, Jun
Shan, Xiaoya
Xiu, Xianchao
Optimization and Control
In this paper, we propose a fast and convergent algorithm to solve unassigned distance geometry problems (uDGP). Technically, we construct a novel quadratic measurement model by leveraging $\ell_0$-norm instead of $\ell_1$-norm in the literature. To solve the nonconvex model, we establish its optimality conditions and develop a fast iterative hard thresholding (IHT) algorithm. Theoretically, we rigorously prove that the whole generated sequence converges to the L-stationary point with the help of the Kurdyka-Lojasiewicz (KL) property. Numerical studies on the turnpike and beltway problems validate its superiority over existing $\ell_1$-norm-based method.
title A Fast and Convergent Algorithm for Unassigned Distance Geometry Problems
topic Optimization and Control
url https://arxiv.org/abs/2502.02280