RegOT is a collection of state-of-the-art solvers for regularized optimal transport (OT) problems, implemented in efficient C++ code. This repository is the Python interface to RegOT.

RegOT mainly solves two types of regularized OT problems: the entropic-regularized OT (EROT) and the quadratically regularized OT (QROT).

EROT, also known as the Sinkhorn-type OT, considers the following optimization problem:

where $a\in\mathbb{R}^n$ and $b\in\mathbb{R}^m$ are two given probability vectors with $a_i>0$, $b_j>0$, $\sum_{i=1}^n a_i=\sum_{j=1}^m b_j=1$, and $M\in\mathbb{R}^{n\times m}$ is a given cost matrix. The function $h(T)=\sum_{i=1}^{n}\sum_{j=1}^{m}T_{ij}(1-\log T_{ij})$ is the entropy term, and $\eta>0$ is a regularization parameter.

QROT, also known as the Euclidean-regularized OT, is concerned with the problem

Currently RegOT contains the following solvers for EROT (methods marked with 🌟 are developed by our group!):

• sinkhorn_bcd: the block coordinate descent (BCD) algorithm, equivalent to the well-known Sinkhorn algorithm.
• sinkhorn_apdagd: the adaptive primal-dual accelerate gradient descent (APDAGD) algorithm (link to paper).
• sinkhorn_lbfgs_dual: the L-BFGS algorithm applied to the dual problem of EROT.
• sinkhorn_newton: Newton's method applied to the dual problem of EROT.
• 🌟sinkhorn_sparse_newton: Newton-type method using sparsified Hessian matrix, as described in our SPLR paper.
• 🌟sinkhorn_ssns: the safe and sparse Newton method for Sinkhorn-type OT (SSNS, link to paper).
• 🌟sinkhorn_splr: the sparse-plus-low-rank quasi-Newton method for the dual problem of EROT (SPLR, link to paper).

The following solvers are available for the QROT problem:

• qrot_bcd: the BCD algorithm.
• qrot_gd: the line search gradient descent algorithm applied to the dual problem of QROT.
• qrot_apdagd: the APDAGD algorithm (link to paper).
• qrot_pdaam: the primal-dual accelerated alternating minimization (PDAAM) algorithm (link to paper).
• qrot_lbfgs_dual: the L-BFGS algorithm applied to the dual problem of QROT.
• qrot_lbfgs_semi_dual: the L-BFGS algorithm applied to the semi-dual problem of QROT (link to paper).
• qrot_assn: the adaptive semi-smooth Newton (ASSN) method applied to the dual problem of QROT (link to paper).
• qrot_grssn: the globalized and regularized semi-smooth Newton (GRSSN) method applied to the dual problem of QROT (link to paper).

You can simply install RegOT using the pip command:

pip install regot

A C++ compiler is needed to build RegOT from source. Enter the source directory and run

pip install . -r requirements.txt

The code below shows a minimal example computing EROT given $a$, $b$, $M$, and $\eta$.

import numpy as np
from scipy.stats import expon, norm
import regot
import matplotlib.pyplot as plt

# OT between two discretized distributions
# One is exponential, the other is mixture normal
def example(n=100, m=80):
    x1 = np.linspace(0.0, 5.0, num=n)
    x2 = np.linspace(0.0, 5.0, num=m)
    distr1 = expon(scale=1.0)
    distr2 = norm(loc=1.0, scale=0.2)
    distr3 = norm(loc=3.0, scale=0.5)
    a = distr1.pdf(x1)
    a = a / np.sum(a)
    b = 0.2 * distr2.pdf(x2) + 0.8 * distr3.pdf(x2)
    b = b / np.sum(b)
    M = np.square(x1.reshape(n, 1) - x2.reshape(1, m))
    return M, a, b

# Source and target distribution vectors `a` and `b`
# Cost matrix `M`
# Regularization parameter `reg`
np.random.seed(123)
M, a, b = example(n=100, m=80)
reg = 0.1

# Algorithm: block coordinate descent (the Sinkhorn algorithm)
res1 = regot.sinkhorn_bcd(
    M, a, b, reg, tol=1e-6, max_iter=1000, verbose=1)

# Algorithm: SSNS
reg = 0.01
res2 = regot.sinkhorn_ssns(
    M, a, b, reg, tol=1e-6, max_iter=1000, verbose=0)

We can retrieve the computed transport plans and visualize them:

def vis_plan(T, title=""):
    fig = plt.figure(figsize=(8, 8))
    plt.imshow(T, interpolation="nearest")
    plt.title(title, fontsize=20)
    plt.show()

vis_plan(res1.plan, title="reg=0.1")
vis_plan(res2.plan, title="reg=0.01")

Please consider to cite our work if you find our algorithms or software useful in your research and applications.

@inproceedings{tang2024safe,
  title={Safe and sparse Newton method for entropic-regularized optimal transport},
  author={Tang, Zihao and Qiu, Yixuan},
  booktitle={Advances in Neural Information Processing Systems},
  volume={37},
  pages={129914--129943},
  year={2024}
}

@inproceedings{wang2025sparse,
  title={The Sparse-Plus-Low-Rank quasi-Newton method for entropic-regularized optimal transport},
  author={Wang, Chenrui and Qiu, Yixuan},
  booktitle={Forty-second International Conference on Machine Learning},
  year={2025}
}