Local Search for Integer Quadratic Programming
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917789719068672 |
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| author | He, Xiang Lin, Peng Cai, Shaowei |
| author_facet | He, Xiang Lin, Peng Cai, Shaowei |
| contents | Integer Quadratic Programming (IQP) is an important problem in operations research. Local search is a powerful method for solving hard problems, but the research on local search algorithms for IQP solving is still on its early stage. This paper develops an efficient local search solver for solving general IQP, called LS-IQCQP. We propose four new local search operators for IQP that can handle quadratic terms in the objective function, constraints or both. Furthermore, a two-mode local search algorithm is introduced, utilizing newly designed scoring functions to enhance the search process. Experiments are conducted on standard IQP benchmarks QPLIB and MINLPLIB, comparing LS-IQCQP with several state-of-the-art IQP solvers. Experimental results demonstrate that LS-IQCQP is competitive with the most powerful commercial solver Gurobi and outperforms other state-of-the-art solvers. Moreover, LS-IQCQP has established 6 new records for QPLIB and MINLPLIB open instances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19668 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Local Search for Integer Quadratic Programming He, Xiang Lin, Peng Cai, Shaowei Artificial Intelligence Integer Quadratic Programming (IQP) is an important problem in operations research. Local search is a powerful method for solving hard problems, but the research on local search algorithms for IQP solving is still on its early stage. This paper develops an efficient local search solver for solving general IQP, called LS-IQCQP. We propose four new local search operators for IQP that can handle quadratic terms in the objective function, constraints or both. Furthermore, a two-mode local search algorithm is introduced, utilizing newly designed scoring functions to enhance the search process. Experiments are conducted on standard IQP benchmarks QPLIB and MINLPLIB, comparing LS-IQCQP with several state-of-the-art IQP solvers. Experimental results demonstrate that LS-IQCQP is competitive with the most powerful commercial solver Gurobi and outperforms other state-of-the-art solvers. Moreover, LS-IQCQP has established 6 new records for QPLIB and MINLPLIB open instances. |
| title | Local Search for Integer Quadratic Programming |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2409.19668 |