Source code for ot.lp.cvx

# -*- coding: utf-8 -*-
LP solvers for optimal transport using cvxopt

# Author: Remi Flamary <>
# License: MIT License

import numpy as np
import scipy as sp
import scipy.sparse as sps

    import cvxopt
    from cvxopt import solvers, matrix, spmatrix
except ImportError:
    cvxopt = False

def scipy_sparse_to_spmatrix(A):
    """Efficient conversion from scipy sparse matrix to cvxopt sparse matrix"""
    coo = A.tocoo()
    SP = spmatrix(, coo.row.tolist(), coo.col.tolist(), size=A.shape)
    return SP

[docs]def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-point'): """Compute the Wasserstein barycenter of distributions A The function solves the following optimization problem [16]: .. math:: \mathbf{a} = arg\min_\mathbf{a} \sum_i W_{1}(\mathbf{a},\mathbf{a}_i) where : - :math:`W_1(\cdot,\cdot)` is the Wasserstein distance (see ot.emd.sinkhorn) - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` The linear program is solved using the interior point solver from scipy.optimize. If cvxopt solver if installed it can use cvxopt Note that this problem do not scale well (both in memory and computational time). Parameters ---------- A : np.ndarray (d,n) n training distributions a_i of size d M : np.ndarray (d,d) loss matrix for OT reg : float Regularization term >0 weights : np.ndarray (n,) Weights of each histogram a_i on the simplex (barycentric coodinates) verbose : bool, optional Print information along iterations log : bool, optional record log if True solver : string, optional the solver used, default 'interior-point' use the lp solver from scipy.optimize. None, or 'glpk' or 'mosek' use the solver from cvxopt. Returns ------- a : (d,) ndarray Wasserstein barycenter log : dict log dictionary return only if log==True in parameters References ---------- .. [16] Agueh, M., & Carlier, G. (2011). Barycenters in the Wasserstein space. SIAM Journal on Mathematical Analysis, 43(2), 904-924. """ if weights is None: weights = np.ones(A.shape[1]) / A.shape[1] else: assert(len(weights) == A.shape[1]) n_distributions = A.shape[1] n = A.shape[0] n2 = n * n c = np.zeros((0)) b_eq1 = np.zeros((0)) for i in range(n_distributions): c = np.concatenate((c, M.ravel() * weights[i])) b_eq1 = np.concatenate((b_eq1, A[:, i])) c = np.concatenate((c, np.zeros(n))) lst_idiag1 = [sps.kron(sps.eye(n), np.ones((1, n))) for i in range(n_distributions)] # row constraints A_eq1 = sps.hstack((sps.block_diag(lst_idiag1), sps.coo_matrix((n_distributions * n, n)))) # columns constraints lst_idiag2 = [] lst_eye = [] for i in range(n_distributions): if i == 0: lst_idiag2.append(sps.kron(np.ones((1, n)), sps.eye(n))) lst_eye.append(-sps.eye(n)) else: lst_idiag2.append(sps.kron(np.ones((1, n)), sps.eye(n - 1, n))) lst_eye.append(-sps.eye(n - 1, n)) A_eq2 = sps.hstack((sps.block_diag(lst_idiag2), sps.vstack(lst_eye))) b_eq2 = np.zeros((A_eq2.shape[0])) # full problem A_eq = sps.vstack((A_eq1, A_eq2)) b_eq = np.concatenate((b_eq1, b_eq2)) if not cvxopt or solver in ['interior-point']: # cvxopt not installed or interior point if solver is None: solver = 'interior-point' options = {'sparse': True, 'disp': verbose} sol = sp.optimize.linprog(c, A_eq=A_eq, b_eq=b_eq, method=solver, options=options) x = sol.x b = x[-n:] else: h = np.zeros((n_distributions * n2 + n)) G = -sps.eye(n_distributions * n2 + n) sol = solvers.lp(matrix(c), scipy_sparse_to_spmatrix(G), matrix(h), A=scipy_sparse_to_spmatrix(A_eq), b=matrix(b_eq), solver=solver) x = np.array(sol['x']) b = x[-n:].ravel() if log: return b, sol else: return b