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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2502.01163 |
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| _version_ | 1866912224044384256 |
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| author | Emmerich, Michael |
| author_facet | Emmerich, Michael |
| contents | We present a dynamic programming algorithm for selecting a representative subset of size $k$ from a given set with $n$ points such that the Riesz $s$-energy is near minimized. While NP-hard in general dimensions, the one-dimensional case can use the natural data ordering for efficient dynamic programming as an effective heuristic solution approach. This approach is then extended to problems related to two-dimensional Pareto front representations arising in biobjective optimization problems. Under the assumption of sorted (or non-dominated) input, the method typically yields near-optimal solutions in most cases. We also show that the approach avoids mistakes of greedy subset-selection by means of example. However, as we demonstrate, there are exceptions where DP does not identify the global minimum; for example, in one of our examples, the DP solution slightly deviates from the configuration found by a brute-force search. This is because the DP scheme's recurrence is approximate. The total time complexity of our algorithm is shown to be $O(n^2 k)$. We provide computational examples with discontinuous Pareto fronts and an open-source Python implementation, demonstrating the approximate DP algorithm's effectiveness across various problems with large point sets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01163 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Minimum Riesz s-Energy Subset Selection in Ordered Point Sets via Dynamic Programming Emmerich, Michael Data Structures and Algorithms F.2.2 We present a dynamic programming algorithm for selecting a representative subset of size $k$ from a given set with $n$ points such that the Riesz $s$-energy is near minimized. While NP-hard in general dimensions, the one-dimensional case can use the natural data ordering for efficient dynamic programming as an effective heuristic solution approach. This approach is then extended to problems related to two-dimensional Pareto front representations arising in biobjective optimization problems. Under the assumption of sorted (or non-dominated) input, the method typically yields near-optimal solutions in most cases. We also show that the approach avoids mistakes of greedy subset-selection by means of example. However, as we demonstrate, there are exceptions where DP does not identify the global minimum; for example, in one of our examples, the DP solution slightly deviates from the configuration found by a brute-force search. This is because the DP scheme's recurrence is approximate. The total time complexity of our algorithm is shown to be $O(n^2 k)$. We provide computational examples with discontinuous Pareto fronts and an open-source Python implementation, demonstrating the approximate DP algorithm's effectiveness across various problems with large point sets. |
| title | Minimum Riesz s-Energy Subset Selection in Ordered Point Sets via Dynamic Programming |
| topic | Data Structures and Algorithms F.2.2 |
| url | https://arxiv.org/abs/2502.01163 |