ott.core.discrete_barycenter.discrete_barycenter#

ott.core.discrete_barycenter.discrete_barycenter(geom, a, weights=None, dual_initialization=None, threshold=0.01, norm_error=1, inner_iterations=10, min_iterations=0, max_iterations=2000, lse_mode=True, debiased=False)[source]#

Compute discrete barycenter [Janati et al., 2020].

Parameters
  • geom (Geometry) – a Cost object able to apply kernels with a certain epsilon.

  • a (ndarray) – jnp.ndarray<float>[batch, geom.num_a]: batch of histograms.

  • weights (Optional[ndarray]) – jnp.ndarray of weights in the probability simplex

  • dual_initialization (Optional[ndarray]) – jnp.ndarray, size [batch, num_b] initialization for g_v

  • threshold (float) – (float) tolerance to monitor convergence.

  • norm_error (int) – int, power used to define p-norm of error for marginal/target.

  • inner_iterations (float) – (int32) the Sinkhorn error is not recomputed at each iteration but every inner_num_iter instead to avoid computational overhead.

  • min_iterations (int) – (int32) the minimum number of Sinkhorn iterations carried out before the error is computed and monitored.

  • max_iterations (int) – (int32) the maximum number of Sinkhorn iterations.

  • lse_mode (bool) – True for log-sum-exp computations, False for kernel multiply.

  • debiased (bool) – whether to run the debiased version of the Sinkhorn divergence.

Return type

Barycenter

Returns

A SinkhornBarycenterOutput, which contains two arrays of potentials, each of size batch times geom.num_a, summarizing the OT between each histogram in the database onto the barycenter, described in histogram, as well as a sequence of errors that monitors convergence.