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Numerical software is usually shipped with built-in hyperparameters. Bycarefully tuning those hyperparameters, significant performance enhancementscan be achieved for specific applications. We developed MindOpt Tuner, a newautomatic tuning tool that supports a wide range of numerical software,including optimization and other solvers. MindOpt Tuner uses elastic cloudresources, features a web-based task management panel and integration withipython notebook with both command-line tools and Python APIs. Our experimentswith COIN-OR Cbc, an open-source mixed-integer optimization solver, demonstrateremarkable improvements with the tuned parameters compared to the default oneson the MIPLIB2017 test set, resulting in over 100x acceleration on severalproblem instances. Additionally, the results demonstrate that Tuner has ahigher tuning efficiency compared to the state-of-the-art automatic tuning toolSMAC3.

MindOpt Tuner: Boost the Performance of Numerical Software by Automatic Parameter Tuning