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GlobalSearch-rs

Comparison with Rust

PyGlobalSearch is a Python package that provides an interface to the globalsearch-rs library using PyO3 and maturin. It allows users to leverage the power of Rust’s optimization algorithms within Python, making it easier to integrate into existing Python workflows.

Key Differences

While both PyGlobalSearch and globalsearch-rs aim to provide powerful optimization tools, there are some key differences between the two:

  1. Language Integration: PyGlobalSearch is designed specifically for Python users, providing a more seamless integration with Python’s data structures and libraries. In contrast, globalsearch-rs is a Rust library that may require additional effort to write your optimization problems.

  2. Performance: Since globalsearch-rs is implemented in Rust, it may offer better performance for certain optimization tasks compared to the Python wrapper. However, the actual performance will depend on the specific use case.

  3. Ease of Use: PyGlobalSearch aims to provide a user-friendly interface for Python users, making it easier to set up and use optimization algorithms without needing to understand the underlying Rust implementation.

  4. Community and Ecosystem: PyGlobalSearch benefits from the larger Python ecosystem, allowing users to leverage existing Python libraries and tools, like NumPy for array manipulation and JAX for automatic differentiation.

  5. Limitations in terms of features: PyGlobalSearch does not expose all the features of globalsearch-rs, potentially limiting advanced users who require specific functionality. As of now, the focus is on providing a core set of tools that are most relevant for Python users. We currently don’t support parallelism of stage 1 using rayon nor checkpointing (no feature flags). Also, for the COBYLA local solver, we only support a maximum of 1000 constraints.