Releases · OpenSourceEconomics/pylcm

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:

@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.
@janosg

Designed and implemented the initial prototype of PyLCM, laying the foundation for its
development. He onboarded @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.
@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.
@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.
@mo2561057

Added tests for the model processing and fully discrete models.
@MImmesberger

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