v0.6.0

What has changed

⚠️ This release changes the high-level API ⚠️

Deprecation notice

• bp.run_bp is deprecated in favor of bp.run. bp.run_bp will be removed in future releases.
• Dropped support for python 3.7.

New features

• Added a SDLP solver that solves the smoothed dual of the LP relaxation of the MAP problem by accelerated gradient descent.
  • An alternative to Belief Propagation with convergence guarantees.
  • Based on the Smooth Dual LP-MAP problem introduced in this paper.
  • Leverages existing PGMax infrastructure to implement the underlying message-passing algorithm.
• Added a solver for the primal of the LP relaxation based on the LP solver from cvxpy.

Changes

• Implemented a unified interface for the BP and the SDLP solvers.
  • Solvers can be created via

     inferer = infer.build_inferer(fg.bp_state, backend=BACKEND)

    where BACKEND can be "bp" or "sdlp".
• Added a run_with_diffs function to the BP solver to monitor BP convergence.
• Improved the stability of message updates at low temperatures for logical factors and pool factors.
• Improved the numerical stability of the messages / potentials clipping.
• Improved the handling of various corner cases, including variables with single state, empty variable groups, energy with infinite potentials, etc..

Examples

• Updated all example colabs to use the new unified interface.
• Added a colab notebook which uses the SDLP solver to
  1. Run inference on an Ising Model and compare its results with BP
  2. Extract sparse feature activations from visually complex binary scenes.