Summary of Local Search For Integer Quadratic Programming, by Xiang He et al.
Local Search for Integer Quadratic Programming
by Xiang He, Peng Lin, Shaowei Cai
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel local search algorithm called LS-IQCQP is proposed to efficiently solve general Integer Quadratic Programming (IQP) problems. The method incorporates four new local search operators that can handle quadratic terms in the objective function, constraints, or both. A two-mode local search algorithm is introduced, utilizing scoring functions to enhance the search process. Experimental results on standard IQP benchmarks QPLIB and MINLPLIB show that LS-IQCQP is competitive with the commercial solver Gurobi and outperforms other state-of-the-art solvers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LS-IQCQP is a new way to solve really hard math problems called IQP. It uses local search, which is like searching for something in a big space by looking around you. The algorithm has four special ways of moving around this space that can handle tricky parts of the problem. It also has a special scoring system to help it find the best solution. People tested LS-IQCQP on some standard IQP problems and it did really well compared to other good algorithms. |
Keywords
» Artificial intelligence » Objective function
