Dwave-samplers

New Features

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

Upgrade Notes

- No longer build wheels for Python 3.7.

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](https://github.com/dwavesystems/dwave-samplers/issues/57).

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.

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- Fix the energy in sample sets returned by `PlanarGraphSolver`.

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- 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](https://github.com/dwavesystems/dwave-neal/pull/91).

New Features

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

Bug Fixes

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

Prelude

D-Wave Samplers combines the samplers from [dwave-greedy](https://github.com/dwavesystems/dwave-greedy), [dwave-neal](https://github.com/dwavesystems/dwave-neal), and [dwave-tabu](https://github.com/dwavesystems/dwave-tabu).

New Features

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

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- Add `RandomSampler` which generates samples randomly.