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This is a record of all past PyLCM releases and what went into them in reverse chronological order. We follow semantic versioning.

0.0.1

Initial Release

  • First public release of PyLCM.

  • Includes core functionality:

    • Specification of finite-horizon discrete-continuous choice models with an arbitrary number of discrete and continuous states and actions.

    • Linearly and Log-linearly spaced grids that approximate continuous states and actions.

    • Linear interpolation and extrapolation of the value function for continuous states.

    • Grid search (brute-force) for finding the optimal continuous policy.

    • Stochastic state transitions for discrete states which may depend on other discrete states and actions.

  • Built with contributions from the PyLCM team.

Contributions

Thanks to everyone who contributed to this release:

  • {ghuser}hmgaudecker

    Initiated and drove the development agenda for PyLCM, ensuring strategic direction and alignment. He actively steered the project, facilitated collaboration, and secured funding to support core development. Additionally, he reviewed pull requests and provided feedback on the internal and external code structure and design.

  • {ghuser}janosg

    Designed and implemented the initial prototype of PyLCM, laying the foundation for its development. He onboarded {ghuser}timmens and played a key role in shaping the project's direction. After stepping back from active development, he contributed to implementation discussions and later provided guidance on architectural decisions.

  • {ghuser}timmens

    Took over development of PyLCM, expanding its functionality with key features like the simulation function, extrapolation capabilities, and special arguments. He led extensive refactoring to improve code clarity, maintainability, and testability, making the package easier to develop and extend. His contributions also include improved documentation, type annotations, static type checking, and the introduction of example and explanation notebooks.

  • {ghuser}mj023

    Analyzed and optimized PyLCM's performance on the GPU, profiling execution and examining the computational graph of JAX-compiled functions. He fine-tuned the solve function's just-in-time compilation to reduce runtime and improve efficiency. Additionally, he compared PyLCM's performance against similar libraries, providing insights into its computational efficiency.

  • {ghuser}mo2561057

    Added tests for the model processing and fully discrete models.

  • {ghuser}MImmesberger

    Added checks to test PyLCM's results against analytical solutions.

Early contributors

  • {ghuser}segsell

  • {ghuser}ChristianZimpelmann

  • {ghuser}tobiasraabe