# ot.optim

Generic solvers for regularized OT

## Functions

ot.optim.cg(a, b, M, reg, f, df, G0=None, numItermax=200, numItermaxEmd=100000, stopThr=1e-09, stopThr2=1e-09, verbose=False, log=False, **kwargs)[source]

Solve the general regularized OT problem with conditional gradient

The function solves the following optimization problem:

\begin{align}\begin{aligned}\gamma = \mathop{\arg \min}_\gamma \quad \langle \gamma, \mathbf{M} \rangle_F + \mathrm{reg} \cdot f(\gamma)\\s.t. \ \gamma \mathbf{1} &= \mathbf{a}\\ \gamma^T \mathbf{1} &= \mathbf{b}\\ \gamma &\geq 0\end{aligned}\end{align}

where :

• $$\mathbf{M}$$ is the (ns, nt) metric cost matrix

• $$f$$ is the regularization term (and df is its gradient)

• $$\mathbf{a}$$ and $$\mathbf{b}$$ are source and target weights (sum to 1)

The algorithm used for solving the problem is conditional gradient as discussed in [1]

Parameters
• a (array-like, shape (ns,)) – samples weights in the source domain

• b (array-like, shape (nt,)) – samples in the target domain

• M (array-like, shape (ns, nt)) – loss matrix

• reg (float) – Regularization term >0

• G0 (array-like, shape (ns,nt), optional) – initial guess (default is indep joint density)

• numItermax (int, optional) – Max number of iterations

• numItermaxEmd (int, optional) – Max number of iterations for emd

• stopThr (float, optional) – Stop threshold on the relative variation (>0)

• stopThr2 (float, optional) – Stop threshold on the absolute variation (>0)

• verbose (bool, optional) – Print information along iterations

• log (bool, optional) – record log if True

• **kwargs (dict) – Parameters for linesearch

Returns

• gamma ((ns x nt) ndarray) – Optimal transportation matrix for the given parameters

• log (dict) – log dictionary return only if log==True in parameters

References

1

Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.

ot.lp.emd

Unregularized optimal ransport

ot.bregman.sinkhorn

Entropic regularized optimal transport

### Examples using ot.optim.cg

ot.optim.gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, numInnerItermax=200, stopThr=1e-09, stopThr2=1e-09, verbose=False, log=False)[source]

Solve the general regularized OT problem with the generalized conditional gradient

The function solves the following optimization problem:

\begin{align}\begin{aligned}\gamma = \mathop{\arg \min}_\gamma \quad \langle \gamma, \mathbf{M} \rangle_F + \mathrm{reg_1}\cdot\Omega(\gamma) + \mathrm{reg_2}\cdot f(\gamma)\\s.t. \ \gamma \mathbf{1} &= \mathbf{a}\\ \gamma^T \mathbf{1} &= \mathbf{b}\\ \gamma &\geq 0\end{aligned}\end{align}

where :

• $$\mathbf{M}$$ is the (ns, nt) metric cost matrix

• $$\Omega$$ is the entropic regularization term $$\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})$$

• $$f$$ is the regularization term (and df is its gradient)

• $$\mathbf{a}$$ and $$\mathbf{b}$$ are source and target weights (sum to 1)

The algorithm used for solving the problem is the generalized conditional gradient as discussed in [5, 7]

Parameters
• a (array-like, shape (ns,)) – samples weights in the source domain

• b (array-like, (nt,)) – samples in the target domain

• M (array-like, shape (ns, nt)) – loss matrix

• reg1 (float) – Entropic Regularization term >0

• reg2 (float) – Second Regularization term >0

• G0 (array-like, shape (ns, nt), optional) – initial guess (default is indep joint density)

• numItermax (int, optional) – Max number of iterations

• numInnerItermax (int, optional) – Max number of iterations of Sinkhorn

• stopThr (float, optional) – Stop threshold on the relative variation (>0)

• stopThr2 (float, optional) – Stop threshold on the absolute variation (>0)

• verbose (bool, optional) – Print information along iterations

• log (bool, optional) – record log if True

Returns

• gamma (ndarray, shape (ns, nt)) – Optimal transportation matrix for the given parameters

• log (dict) – log dictionary return only if log==True in parameters

References

5
1. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, “Optimal Transport for Domain Adaptation,” in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1

7

Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.

ot.optim.cg

### Examples using ot.optim.gcg

ot.optim.line_search_armijo(f, xk, pk, gfk, old_fval, args=(), c1=0.0001, alpha0=0.99, alpha_min=None, alpha_max=None)[source]

Armijo linesearch function that works with matrices

Find an approximate minimum of $$f(x_k + \alpha \cdot p_k)$$ that satisfies the armijo conditions.

Parameters
• f (callable) – loss function

• xk (array-like) – initial position

• pk (array-like) – descent direction

• gfk (array-like) – gradient of f at $$x_k$$

• old_fval (float) – loss value at $$x_k$$

• args (tuple, optional) – arguments given to f

• c1 (float, optional) – $$c_1$$ const in armijo rule (>0)

• alpha0 (float, optional) – initial step (>0)

• alpha_min (float, optional) – minimum value for alpha

• alpha_max (float, optional) – maximum value for alpha

Returns

• alpha (float) – step that satisfy armijo conditions

• fc (int) – nb of function call

• fa (float) – loss value at step alpha

For any convex or non-convex 1d quadratic function f, solve the following problem:

$\mathop{\arg \min}_{0 \leq x \leq 1} \quad f(x) = ax^{2} + bx + c$
Parameters
• a (float) – The coefficients of the quadratic function

• b (float) – The coefficients of the quadratic function

• c (float) – The coefficients of the quadratic function

Returns

x – The optimal value which leads to the minimal cost

Return type

float

ot.optim.solve_linesearch(cost, G, deltaG, Mi, f_val, armijo=True, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None, alpha_min=None, alpha_max=None)[source]

Solve the linesearch in the FW iterations

Parameters
• cost (method) – Cost in the FW for the linesearch

• G (array-like, shape(ns,nt)) – The transport map at a given iteration of the FW

• deltaG (array-like (ns,nt)) – Difference between the optimal map found by linearization in the FW algorithm and the value at a given iteration

• Mi (array-like (ns,nt)) – Cost matrix of the linearized transport problem. Corresponds to the gradient of the cost

• f_val (float) – Value of the cost at G

• armijo (bool, optional) – If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False.

• C1 (array-like (ns,ns), optional) – Structure matrix in the source domain. Only used and necessary when armijo=False

• C2 (array-like (nt,nt), optional) – Structure matrix in the target domain. Only used and necessary when armijo=False

• reg (float, optional) – Regularization parameter. Only used and necessary when armijo=False

• Gc (array-like (ns,nt)) – Optimal map found by linearization in the FW algorithm. Only used and necessary when armijo=False

• constC (array-like (ns,nt)) – Constant for the gromov cost. See [24]. Only used and necessary when armijo=False

• M (array-like (ns,nt), optional) – Cost matrix between the features. Only used and necessary when armijo=False

• alpha_min (float, optional) – Minimum value for alpha

• alpha_max (float, optional) – Maximum value for alpha

Returns

• alpha (float) – The optimal step size of the FW

• fc (int) – nb of function call. Useless here

• f_val (float) – The value of the cost for the next iteration

References

24

Vayer Titouan, Chapel Laetitia, Flamary Rémi, Tavenard Romain and Courty Nicolas “Optimal Transport for structured data with application on graphs” International Conference on Machine Learning (ICML). 2019.