1.4.0

New Features

• Support Python 3.13.

Upgrade Notes

• Drop support for Python 3.8.

1.3.0

New Features

• Support, and build with, NumPy 2.0

Upgrade Notes

• Require dimod>=0.12.13.
• Require NetworkX>=3.0.

1.2.0

New Features

• Build C++ extensions with Cython 3.
• Build wheels for Python 3.12.

Upgrade Notes

• No longer build wheels for Python 3.7.

1.1.0

New Features

• Add timing information for SteepestDescentSolver.
• Add timing information for SimulatedAnnealingSampler.
• Add randomize_order keyword argument to the SimulatedAnnealingSampler.sample() method. This controls whether the variables are updated in a random order or not.
• Add proposal_acceptance_criteria keyword argument to the SimulatedAnnealingSampler.sample() method. This controls whether the sampler will use Gibbs or Metropolis updates.

Upgrade Notes

• Build extensions with C++17.

Bug Fixes

• Fix ImportError being raised on Windows when other dwave namespace packages are installed after dwave-samplers. See #57.

1.0.0

New Features

• Build wheels for Python 3.11

Upgrade Notes

• Change the default value of the TreeDecompositionSampler.sample() beta keyword argument to 1.

Bug Fixes

• Fix greedy and simulated annealing performance in wheels compiled for linux aarch64 systems.

• Fix the energy in sample sets returned by PlanarGraphSolver.

• Fix several errors and inconsistencies in the calculation of the simulated annealing algorithm's beta schedule. Note that for very small problems this may result in slower execution, because the schedule no longer does a discretization of 1000 sweeps at each beta. See dwavesystems/dwave-neal#91.

1.0.0.dev2

New Features

• Add PlanarGraphSolver, an exact solver for planar Ising problems with no linear biases.

1.0.0.dev1

Bug Fixes

• Allow num_sweeps=0 for simulated annealing. This was incorrectly disabled in 1.0.0.dev0.

1.0.0.dev0

Prelude

D-Wave Samplers combines the samplers from dwave-greedy, dwave-neal, and dwave-tabu.

New Features

• Add TreeDecompositionSampler and TreeDecompositionSolver which solve/sample low treewidth problems.

• Add RandomSampler which generates samples randomly.