RegOT-Python
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.
📝 Formulation
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:
\begin{align*}
\min_{T\in\mathbb{R}^{n\times m}}\quad & \langle T,M\rangle-\eta h(T),\\
\text{subject to}\quad & T\mathbf{1}_{m}=a,T^{T}\mathbf{1}_{n}=b,T\ge0,
\end{align*}
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
\begin{align*}
\min_{T\in\mathbb{R}^{n\times m}}\quad & \langle T,M\rangle+(\gamma/2) \Vert T \Vert_F^2,\\
\text{subject to}\quad & T\mathbf{1}_{m}=a,T^{T}\mathbf{1}_{n}=b,T\ge0.
\end{align*}
🔧 Solvers
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).
💽 Installation
Using pip
You can simply install RegOT using the pip command:
pip install regot
Building from source
A C++ compiler is needed to build RegOT from source. Enter the source directory and run
pip install . -r requirements.txt
📗 Example
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")
Metadata
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