v1.2.2

Release Notes

Highlights

• Added plotting capabilities for stochastic constraint problems, along with the new FCSA solver, the SAN-2 problem, and a corresponding demo notebook.

• Refactored the solver, problem, and model classes, improved budget tracking, migrated configuration to Pydantic, and simplified how problem and solver metadata are specified.

• Replaced the Ruby-based NOLHS generator with a native Python implementation integrated into the GUI and data-farming workflow.

• Introduced input-model hooks that allow simulations to load data from custom sources, with a new data-driven walkthrough notebook.

• Added a Rust-based MRG32K3A random number generator, optionally enabled via MRG32K3A_BACKEND=rust; this will become the default in a future release.

• Added experimental support for patching simulation model with Rust implementation. This can be enabled by environment variable SIMOPT_EXT=simopt_extensions, where simopt_extensions is a user module that provides a patch_model* function.

Breaking Changes

• Python 3.11 is now the minimum supported version.

• Solver, problem, and model configuration has moved to Pydantic models and a new API; downstream scripts must adopt the updated schema.

Docs & Examples

• Comprehensive documentation overhaul (available at https://simopt.readthedocs.io/) complete with in-depth model/problem/solver documentation, API reference, and testing guides.

• Demos are maintained as notebooks and Python scripts, and can be executed either in Jupyter or as standalone scripts.

Compared to v1.2.0

• Fixed several bugs in v1.2.0

v1.2.1

Release Notes

Highlights

• Added plotting capabilities for stochastic constraint problems, along with the new FCSA solver, the SAN-2 problem, and a corresponding demo notebook.

• Refactored the solver, problem, and model classes, improved budget tracking, migrated configuration to Pydantic, and simplified how problem and solver metadata are specified.

• Replaced the Ruby-based NOLHS generator with a native Python implementation integrated into the GUI and data-farming workflow.

• Introduced input-model hooks that allow simulations to load data from custom sources, with a new data-driven walkthrough notebook.

• Added a Rust-based MRG32K3A random number generator, optionally enabled via MRG32K3A_BACKEND=rust; this will become the default in a future release.

Breaking Changes

• Python 3.11 is now the minimum supported version.

• Solver, problem, and model configuration has moved to Pydantic models and a new API; downstream scripts must adopt the updated schema.

Docs & Examples

• Comprehensive documentation overhaul (available at https://simopt.readthedocs.io/) complete with in-depth model/problem/solver documentation, API reference, and testing guides.

• Demos are maintained as notebooks and Python scripts, and can be executed either in Jupyter or as standalone scripts.

Compared to v1.2.0

• Fixed several bugs in v1.2.0

v1.2.0

Release Notes

Highlights

• Added plotting capabilities for stochastic constraint problems, along with the new FCSA solver, the SAN-2 problem, and a corresponding demo notebook.

• Refactored the solver, problem, and model classes, improved budget tracking, migrated configuration to Pydantic, and simplified how problem and solver metadata are specified.

• Replaced the Ruby-based NOLHS generator with a native Python implementation integrated into the GUI and data-farming workflow.

• Introduced input-model hooks that allow simulations to load data from custom sources, with a new data-driven walkthrough notebook.

• Added a Rust-based MRG32K3A random number generator, optionally enabled via MRG32K3A_BACKEND=rust; this will become the default in a future release.

Breaking Changes

• Python 3.11 is now the minimum supported version.

• Solver, problem, and model configuration has moved to Pydantic models and a new API; downstream scripts must adopt the updated schema.

Docs & Examples

• Comprehensive documentation overhaul (available at https://simopt.readthedocs.io/) complete with in-depth model/problem/solver documentation, API reference, and testing guides.

• Demos are maintained as notebooks and Python scripts, and can be executed either in Jupyter or as standalone scripts.

v1.1.1

PyPI package v1.1.1.

v1.1.0

PyPI package v1.1.0.

v1.0.2

PyPI package v1.0.2.

v1.0.1

PyPI package v1.0.1.

v1.0.0

PyPI package v1.0.0.