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<!DOCTYPE html>

<head>

<title>ot.regpath — POT Python Optimal Transport 0.9.8.dev documentation</title>

</head>

POT Python Optimal Transport

</a>

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<li class="breadcrumb-item"><a href="../index.html">Module code</a></li>

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<h1>Source code for ot.regpath</h1><div class="highlight"><pre>

# -*- coding: utf-8 -*-

"""

Regularization path OT solvers

"""

# Author: Haoran Wu <haoran.wu@univ-ubs.fr>

# License: MIT License

import warnings

import numpy as np

import scipy.sparse as sp

def recast_ot_as_lasso(a, b, C):

r"""This function recasts the l2-penalized UOT problem as a Lasso problem.

Recall the l2-penalized UOT problem defined in

:ref:`[41] <references-regpath>`

.. math::

\text{UOT}_{\lambda} = \min_T <C, T> + \lambda \|T 1_m -

\mathbf{a}\|_2^2 +

\lambda \|T^T 1_n - \mathbf{b}\|_2^2

s.t.

T \geq 0

where :

- :math:`C` is the cost matrix

- :math:`\lambda` is the l2-regularization parameter

- :math:`\mathbf{a}` and :math:`\mathbf{b}` are the source and target \

distributions

- :math:`T` is the transport plan to optimize

The problem above can be reformulated as a non-negative penalized

linear regression problem, particularly Lasso

.. math::

\text{UOT2}_{\lambda} = \min_{\mathbf{t}} \gamma \mathbf{c}^T

\mathbf{t} + 0.5 * \|H \mathbf{t} - \mathbf{y}\|_2^2

s.t.

\mathbf{t} \geq 0

where :

- :math:`\mathbf{c}` is the flattened version of the cost matrix :math:`C`

- :math:`\mathbf{y}` is the concatenation of vectors :math:`\mathbf{a}` \

and :math:`\mathbf{b}`

- :math:`H` is a metric matrix, see :ref:`[41] <references-regpath>` for \

the design of :math:`H`. The matrix product :math:`H\mathbf{t}` \

computes both the source marginal and the target marginals.

- :math:`\mathbf{t}` is the flattened version of the transport plan \

:math:`T`

Parameters

----------

a : np.ndarray (dim_a,)

Histogram of dimension dim_a

b : np.ndarray (dim_b,)

Histogram of dimension dim_b

C : np.ndarray, shape (dim_a, dim_b)

Cost matrix

Returns

-------

H : np.ndarray (dim_a+dim_b, dim_a*dim_b)

Design matrix that contains only 0 and 1

y : np.ndarray (ns + nt, )

Concatenation of histograms :math:`\mathbf{a}` and :math:`\mathbf{b}`

c : np.ndarray (ns * nt, )

Flattened array of the cost matrix

Examples

--------

>>> import ot

>>> a = np.array([0.2, 0.3, 0.5])

>>> b = np.array([0.1, 0.9])

>>> C = np.array([[16., 25.], [28., 16.], [40., 36.]])

>>> H, y, c = ot.regpath.recast_ot_as_lasso(a, b, C)

>>> H.toarray()

array([[1., 1., 0., 0., 0., 0.],

[0., 0., 1., 1., 0., 0.],

[0., 0., 0., 0., 1., 1.],

[1., 0., 1., 0., 1., 0.],

[0., 1., 0., 1., 0., 1.]])

>>> y

array([0.2, 0.3, 0.5, 0.1, 0.9])

>>> c

array([16., 25., 28., 16., 40., 36.])

References

----------

.. [41] Chapel, L., Flamary, R., Wu, H., Févotte, C., and Gasso, G. (2021).

Unbalanced optimal transport through non-negative penalized

linear regression. NeurIPS.

"""

dim_a = np.shape(a)[0]

dim_b = np.shape(b)[0]

y = np.concatenate((a, b))

c = C.flatten()

jHa = np.arange(dim_a * dim_b)

iHa = np.repeat(np.arange(dim_a), dim_b)

jHb = np.arange(dim_a * dim_b)

iHb = np.tile(np.arange(dim_b), dim_a) + dim_a

j = np.concatenate((jHa, jHb))

i = np.concatenate((iHa, iHb))

H = sp.csc_matrix(

(np.ones(dim_a * dim_b * 2), (i, j)), shape=(dim_a + dim_b, dim_a * dim_b)

)

return H, y, c</div>

def recast_semi_relaxed_as_lasso(a, b, C):

r"""This function recasts the semi-relaxed l2-UOT problem as Lasso problem.

.. math::

\text{semi-relaxed UOT} = \min_T <C, T>

+ \lambda \|T 1_m - \mathbf{a}\|_2^2

s.t.

T^T 1_n = \mathbf{b}

\mathbf{t} \geq 0

where :

- :math:`C` is the metric cost matrix

- :math:`\lambda` is the l2-regularization parameter

- :math:`\mathbf{a}` and :math:`\mathbf{b}` are the source and target \

distributions

- :math:`T` is the transport plan to optimize

The problem above can be reformulated as follows

.. math::

\text{semi-relaxed UOT2} = \min_t \gamma \mathbf{c}^T t

+ 0.5 * \|H_r \mathbf{t} - \mathbf{a}\|_2^2

s.t.

H_c \mathbf{t} = \mathbf{b}

\mathbf{t} \geq 0

where :

- :math:`\mathbf{c}` is flattened version of the cost matrix :math:`C`

- :math:`\gamma = 1/\lambda` is the l2-regularization parameter

- :math:`H_r` is a metric matrix which computes the sum along the \

rows of the transport plan :math:`T`

- :math:`H_c` is a metric matrix which computes the sum along the \

columns of the transport plan :math:`T`

- :math:`\mathbf{t}` is the flattened version of :math:`T`

Parameters

----------

a : np.ndarray (dim_a,)

Histogram of dimension dim_a

b : np.ndarray (dim_b,)

Histogram of dimension dim_b

C : np.ndarray, shape (dim_a, dim_b)

Cost matrix

Returns

-------

Hr : np.ndarray (dim_a, dim_a * dim_b)

Auxiliary matrix constituted by 0 and 1, which computes

the sum along the rows of transport plan :math:`T`

Hc : np.ndarray (dim_b, dim_a * dim_b)

Auxiliary matrix constituted by 0 and 1, which computes

the sum along the columns of transport plan :math:`T`

c : np.ndarray (ns * nt, )

Flattened array of the cost matrix

Examples

--------

>>> import ot

>>> a = np.array([0.2, 0.3, 0.5])

>>> b = np.array([0.1, 0.9])

>>> C = np.array([[16., 25.], [28., 16.], [40., 36.]])

>>> Hr,Hc,c = ot.regpath.recast_semi_relaxed_as_lasso(a, b, C)

>>> Hr.toarray()

array([[1., 1., 0., 0., 0., 0.],

[0., 0., 1., 1., 0., 0.],

[0., 0., 0., 0., 1., 1.]])

>>> Hc.toarray()

array([[1., 0., 1., 0., 1., 0.],

[0., 1., 0., 1., 0., 1.]])

>>> c

array([16., 25., 28., 16., 40., 36.])

"""

c = C.flatten()

jHr = np.arange(dim_a * dim_b)

iHr = np.repeat(np.arange(dim_a), dim_b)

jHc = np.arange(dim_a * dim_b)

iHc = np.tile(np.arange(dim_b), dim_a)

Hr = sp.csc_matrix(

(np.ones(dim_a * dim_b), (iHr, jHr)), shape=(dim_a, dim_a * dim_b)

)

Hc = sp.csc_matrix(

(np.ones(dim_a * dim_b), (iHc, jHc)), shape=(dim_b, dim_a * dim_b)

)

return Hr, Hc, c</div>

def ot_next_gamma(phi, delta, HtH, Hty, c, active_index, current_gamma):

r""" This function computes the next value of gamma if a variable

is added in the next iteration of the regularization path.

We look for the largest value of gamma such that

the gradient of an inactive variable vanishes

.. math::

\max_{i \in \bar{A}} \frac{\mathbf{h}_i^T(H_A \phi - \mathbf{y})}

{\mathbf{h}_i^T H_A \delta - \mathbf{c}_i}

where :

- A is the current active set

- :math:`\mathbf{h}_i` is the :math:`i` th column of the design \

matrix :math:`{H}`

- :math:`{H}_A` is the sub-matrix constructed by the columns of \

:math:`{H}` whose indices belong to the active set A

- :math:`\mathbf{c}_i` is the :math:`i` th element of the cost vector \

:math:`\mathbf{c}`

- :math:`\mathbf{y}` is the concatenation of the source and target \

distributions

- :math:`\phi` is the intercept of the solutions at the current iteration

- :math:`\delta` is the slope of the solutions at the current iteration

Parameters

----------

phi : np.ndarray (size(A), )

Intercept of the solutions at the current iteration

delta : np.ndarray (size(A), )

Slope of the solutions at the current iteration

HtH : np.ndarray (dim_a * dim_b, dim_a * dim_b)

Matrix product of :math:`{H}^T {H}`

Hty : np.ndarray (dim_a + dim_b, )

Matrix product of :math:`{H}^T \mathbf{y}`

c: np.ndarray (dim_a * dim_b, )

Flattened array of the cost matrix :math:`{C}`

active_index : list

Indices of active variables

current_gamma : float

Value of the regularization parameter at the beginning of the current \

iteration

Returns

-------

next_gamma : float

Value of gamma if a variable is added to active set in next iteration

next_active_index : int

Index of variable to be activated

References

----------

.. [41] Chapel, L., Flamary, R., Wu, H., Févotte, C., and Gasso, G. (2021).

Unbalanced optimal transport through non-negative penalized

linear regression. NeurIPS.

"""

M = (HtH[:, active_index].dot(phi) - Hty) / (

HtH[:, active_index].dot(delta) - c + 1e-16

)

M[active_index] = 0

M[M > (current_gamma - 1e-10 * current_gamma)] = 0

return np.max(M), np.argmax(M)</div>

def semi_relaxed_next_gamma(

phi, delta, phi_u, delta_u, HrHr, Hc, Hra, c, active_index, current_gamma

):

r""" This function computes the next value of gamma when a variable is

active in the regularization path of semi-relaxed UOT.

By taking the Lagrangian form of the problem, we obtain a similar update

as the two-sided relaxed UOT

.. math::

\max_{i \in \bar{A}} \frac{\mathbf{h}_{ri}^T(H_{rA} \phi - \mathbf{a})

+ \mathbf{h}_{c i}^T\phi_u}{\mathbf{h}_{r i}^T H_{r A} \delta + \

\mathbf{h}_{c i} \delta_u - \mathbf{c}_i}

where :

- A is the current active set

- :math:`\mathbf{h}_{r i}` is the ith column of the matrix :math:`H_r`

- :math:`\mathbf{h}_{c i}` is the ith column of the matrix :math:`H_c`

- :math:`H_{r A}` is the sub-matrix constructed by the columns of \

:math:`H_r` whose indices belong to the active set A

- :math:`\mathbf{c}_i` is the :math:`i` th element of cost vector \

:math:`\mathbf{c}`

- :math:`\phi` is the intercept of the solutions in current iteration

- :math:`\delta` is the slope of the solutions in current iteration

- :math:`\phi_u` is the intercept of Lagrange parameter at the \

current iteration

- :math:`\delta_u` is the slope of Lagrange parameter at the \

current iteration

Parameters

----------

phi : np.ndarray (size(A), )

Intercept of the solutions at the current iteration

delta : np.ndarray (size(A), )

Slope of the solutions at the current iteration

phi_u : np.ndarray (dim_b, )

Intercept of the Lagrange parameter at the current iteration

delta_u : np.ndarray (dim_b, )

Slope of the Lagrange parameter at the current iteration

HrHr : np.ndarray (dim_a * dim_b, dim_a * dim_b)

Matrix product of :math:`H_r^T H_r`

Hc : np.ndarray (dim_b, dim_a * dim_b)

Matrix that computes the sum along the columns of the transport plan \

:math:`T`

Hra : np.ndarray (dim_a * dim_b, )

Matrix product of :math:`H_r^T \mathbf{a}`

c: np.ndarray (dim_a * dim_b, )

Flattened array of cost matrix :math:`C`

active_index : list

Indices of active variables

current_gamma : float

Value of regularization coefficient at the start of current iteration

Returns

-------

next_gamma : float

Value of gamma if a variable is added to active set in next iteration

next_active_index : int

Index of variable to be activated

References

----------

.. [41] Chapel, L., Flamary, R., Wu, H., Févotte, C., and Gasso, G. (2021).

Unbalanced optimal transport through non-negative penalized

linear regression. NeurIPS.

"""

M = (HrHr[:, active_index].dot(phi) - Hra + Hc.T.dot(phi_u)) / (

HrHr[:, active_index].dot(delta) - c + Hc.T.dot(delta_u) + 1e-16

)

M[active_index] = 0

def compute_next_removal(phi, delta, current_gamma):

r""" This function computes the next gamma value if a variable

is removed at the next iteration of the regularization path.

We look for the largest value of the regularization parameter such that

an element of the current solution vanishes

.. math::

\max_{j \in A} \frac{\phi_j}{\delta_j}

where :

- A is the current active set

- :math:`\phi_j` is the :math:`j` th element of the intercept of the \

current solution

- :math:`\delta_j` is the :math:`j` th element of the slope of the \

current solution

Parameters

----------

phi : ndarray, shape (size(A), )

Intercept of the solution at the current iteration

delta : ndarray, shape (size(A), )

Slope of the solution at the current iteration

current_gamma : float

Value of the regularization parameter at the beginning of the \

current iteration

Returns

-------

next_removal_gamma : float

Gamma value if a variable is removed at the next iteration

next_removal_index : int

Index of the variable to be removed at the next iteration

.. _references-regpath:

References

----------

.. [41] Chapel, L., Flamary, R., Wu, H., Févotte, C., and Gasso, G. (2021).

Unbalanced optimal transport through non-negative penalized

linear regression. NeurIPS.

"""

r_candidate = phi / (delta - 1e-16)

r_candidate[r_candidate >= (1 - 1e-8) * current_gamma] = 0

return np.max(r_candidate), np.argmax(r_candidate)</div>

def complement_schur(M_current, b, d, id_pop):

r""" This function computes the inverse of the design matrix in the \

regularization path using the Schur complement. Two cases may arise:

Case 1: one variable is added to the active set

.. math::

M_{k+1}^{-1} =

\begin{bmatrix}

M_{k}^{-1} + s^{-1} M_{k}^{-1} \mathbf{b} \mathbf{b}^T M_{k}^{-1} \

& - M_{k}^{-1} \mathbf{b} s^{-1} \\

- s^{-1} \mathbf{b}^T M_{k}^{-1} & s^{-1}

\end{bmatrix}

where :

- :math:`M_k^{-1}` is the inverse of the design matrix :math:`H_A^tH_A` \

of the previous iteration

- :math:`\mathbf{b}` is the last column of :math:`M_{k}`

- :math:`s` is the Schur complement, given by \

:math:`s = \mathbf{d} - \mathbf{b}^T M_{k}^{-1} \mathbf{b}`

Case 2: one variable is removed from the active set.

.. math::

M_{k+1}^{-1} = M^{-1}_{k \backslash q} -

\frac{r_{-q,q} r^{T}_{-q,q}}{r_{q,q}}

where :

- :math:`q` is the index of column and row to delete

- :math:`M^{-1}_{k \backslash q}` is the previous inverse matrix deprived \

of the :math:`q` th column and :math:`q` th row

- :math:`r_{-q,q}` is the :math:`q` th column of :math:`M^{-1}_{k}` \

without the :math:`q` th element

- :math:`r_{q, q}` is the element of :math:`q` th column and :math:`q` th \

row in :math:`M^{-1}_{k}`

Parameters

----------

M_current : ndarray, shape (size(A)-1, size(A)-1)

Inverse matrix of :math:`H_A^tH_A` at the previous iteration, with \

size(A) the size of the active set

b : ndarray, shape (size(A)-1, )

None for case 2 (removal), last column of :math:`M_{k}` for case 1 \

(addition)

d : float

should be equal to 2 when UOT and 1 for the semi-relaxed OT

id_pop : int

Index of the variable to be removed, equal to -1

if no variable is deleted at the current iteration

Returns

-------

M : ndarray, shape (size(A), size(A))

Inverse matrix of :math:`H_A^tH_A` of the current iteration

References

----------

.. [41] Chapel, L., Flamary, R., Wu, H., Févotte, C., and Gasso, G. (2021).

Unbalanced optimal transport through non-negative penalized

linear regression. NeurIPS.

"""

if b is None:

b = M_current[id_pop, :]

b = np.delete(b, id_pop)

M_del = np.delete(M_current, id_pop, 0)

a = M_del[:, id_pop]

M_del = np.delete(M_del, id_pop, 1)

M = M_del - np.outer(a, b) / M_current[id_pop, id_pop]

else:

n = b.shape[0] + 1

if np.shape(b)[0] == 0:

M = np.array([[0.5]])

else:

X = M_current.dot(b)

s = d - b.T.dot(X)

# Ensure s is a scalar (extract from array if needed)

if np.ndim(s) > 0:

s = s.item()

M = np.zeros((n, n))

M[:-1, :-1] = M_current + X.dot(X.T) / s

X_ravel = X.ravel()

M[-1, :-1] = -X_ravel / s

M[:-1, -1] = -X_ravel / s

M[-1, -1] = 1 / s

return M</div>

def construct_augmented_H(active_index, m, Hc, HrHr):

r""" This function constructs an augmented matrix for the first iteration

of the semi-relaxed regularization path

.. math::

\text{Augmented}_H =

\begin{bmatrix}

0 & H_{c A} \\

H_{c A}^T & H_{r A}^T H_{r A}

\end{bmatrix}

where :

- :math:`H_{r A}` is the sub-matrix constructed by the columns of \

:math:`H_r` whose indices belong to the active set A

- :math:`H_{c A}` is the sub-matrix constructed by the columns of \

:math:`H_c` whose indices belong to the active set A

Parameters

----------

active_index : list

Indices of the active variables

m : int

Length of the target distribution

Hc : np.ndarray (dim_b, dim_a * dim_b)

Matrix that computes the sum along the columns of the transport plan \

:math:`T`

HrHr : np.ndarray (dim_a * dim_b, dim_a * dim_b)

Matrix product of :math:`H_r^T H_r`

Returns

-------

H_augmented : np.ndarray (dim_b + size(A), dim_b + size(A))

Augmented matrix for the first iteration of the semi-relaxed

regularization path

"""

Hc_sub = Hc[:, active_index].toarray()

HrHr_sub = HrHr[:, active_index]

HrHr_sub = HrHr_sub[active_index, :].toarray()

H_augmented = np.block([[np.zeros((m, m)), Hc_sub], [Hc_sub.T, HrHr_sub]])

return H_augmented</div>

def fully_relaxed_path(a: np.array, b: np.array, C: np.array, reg=1e-4, itmax=50000):

r"""This function gives the regularization path of l2-penalized UOT problem

The problem to optimize is the Lasso reformulation of the l2-penalized UOT:

.. math::

\min_t \gamma \mathbf{c}^T \mathbf{t}

+ 0.5 * \|{H} \mathbf{t} - \mathbf{y}\|_2^2

s.t.

\mathbf{t} \geq 0

where :

- :math:`\mathbf{c}` is the flattened version of the cost matrix \

:math:`{C}`

- :math:`\gamma = 1/\lambda` is the l2-regularization coefficient

- :math:`\mathbf{y}` is the concatenation of vectors :math:`\mathbf{a}` \

and :math:`\mathbf{b}`, defined as \

:math:`\mathbf{y}^T = [\mathbf{a}^T \mathbf{b}^T]`

- :math:`{H}` is a design matrix, see :ref:`[41] <references-regpath>` \

for the design of :math:`{H}`. The matrix product :math:`H\mathbf{t}` \

computes both the source marginal and the target marginals.

- :math:`\mathbf{t}` is the flattened version of the transport matrix

Parameters

----------

a : np.ndarray (dim_a,)

Histogram of dimension dim_a

b : np.ndarray (dim_b,)

Histogram of dimension dim_b

C : np.ndarray, shape (dim_a, dim_b)

Cost matrix

reg: float

l2-regularization coefficient

itmax: int

Maximum number of iteration

Returns

-------

t : np.ndarray (dim_a*dim_b, )

Flattened vector of the optimal transport matrix

t_list : list

List of solutions in the regularization path

gamma_list : list

List of regularization coefficients in the regularization path

Examples

--------

>>> import ot

>>> import numpy as np

>>> n = 3

>>> xs = np.array([1., 2., 3.]).reshape((n, 1))

>>> xt = np.array([5., 6., 7.]).reshape((n, 1))

>>> C = ot.dist(xs, xt)

>>> C /= C.max()

>>> a = np.array([0.2, 0.5, 0.3])

>>> b = np.array([0.2, 0.5, 0.3])

>>> t, _, _ = ot.regpath.fully_relaxed_path(a, b, C, 1e-4)

>>> t

array([1.99958333e-01, 0.00000000e+00, 0.00000000e+00, 3.88888889e-05,

4.99938889e-01, 0.00000000e+00, 0.00000000e+00, 3.88888889e-05,

2.99958333e-01])

References

----------

.. [41] Chapel, L., Flamary, R., Wu, H., Févotte, C., and Gasso, G. (2021).

Unbalanced optimal transport through non-negative penalized

linear regression. NeurIPS.

"""

n = np.shape(a)[0]

m = np.shape(b)[0]

H, y, c = recast_ot_as_lasso(a, b, C)

HtH = H.T.dot(H)

Hty = H.T.dot(y)

n_iter = 1

# initialization

M0 = Hty / c

gamma_list = [np.max(M0)]

active_index = [np.argmax(M0)]

t_list = [np.zeros((n * m,))]

H_inv = np.array([[]])

add_col = np.array([])

id_pop = -1

while n_iter < itmax and gamma_list[-1] > reg:

H_inv = complement_schur(H_inv, add_col, 2.0, id_pop)

current_gamma = gamma_list[-1]

# compute the intercept and slope of solutions in current iteration

# t = phi - gamma * delta

phi = H_inv.dot(Hty[active_index])

delta = H_inv.dot(c[active_index])

gamma, ik = ot_next_gamma(phi, delta, HtH, Hty, c, active_index, current_gamma)

# compute the next lambda when removing a point from the active set

alt_gamma, id_pop = compute_next_removal(phi, delta, current_gamma)

# if the positivity constraint is violated, we remove id_pop

# from active set, otherwise we add ik to active set

if alt_gamma > gamma:

gamma = alt_gamma

else:

id_pop = -1

# compute the solution of current segment

tA = phi - gamma * delta

sol = np.zeros((n * m,))

sol[active_index] = tA

if id_pop != -1:

active_index.pop(id_pop)

add_col = None

else:

active_index.append(ik)

add_col = HtH[active_index[:-1], ik].toarray()

gamma_list.append(gamma)

t_list.append(sol)

n_iter += 1

if itmax <= n_iter:

warnings.warn("Maximum number of iterations has been reached.")

# correct the last solution and gamma

if len(t_list) > 1:

t_final = t_list[-2] + (t_list[-1] - t_list[-2]) * (reg - gamma_list[-2]) / (

gamma_list[-1] - gamma_list[-2]

)

t_list[-1] = t_final

gamma_list[-1] = reg

else:

gamma_list[-1] = reg

warnings.warn("Regularization path does not exist.")

return t_list[-1], t_list, gamma_list</div>

def semi_relaxed_path(a: np.array, b: np.array, C: np.array, reg=1e-4, itmax=50000):

r"""This function gives the regularization path of semi-relaxed

l2-UOT problem.

The problem to optimize is the Lasso reformulation of the l2-penalized UOT:

.. math::

\min_t \gamma \mathbf{c}^T t

+ 0.5 * \|H_r \mathbf{t} - \mathbf{a}\|_2^2

s.t.

H_c \mathbf{t} = \mathbf{b}

\mathbf{t} \geq 0

where :

- :math:`\mathbf{c}` is the flattened version of the cost matrix \

:math:`C`

- :math:`\gamma = 1/\lambda` is the l2-regularization parameter

- :math:`H_r` is a matrix that computes the sum along the rows of \

the transport plan :math:`T`

- :math:`H_c` is a matrix that computes the sum along the columns of \

the transport plan :math:`T`

- :math:`\mathbf{t}` is the flattened version of the transport plan \

:math:`T`

Parameters

----------

a : np.ndarray (dim_a,)

Histogram of dimension dim_a

b : np.ndarray (dim_b,)

Histogram of dimension dim_b

C : np.ndarray, shape (dim_a, dim_b)

Cost matrix

reg: float (optional)

l2-regularization coefficient

itmax: int (optional)

Maximum number of iteration

Returns

-------

t : np.ndarray (dim_a*dim_b, )

Flattened vector of the (unregularized) optimal transport matrix

t_list : list

List of all the optimal transport vectors of the regularization path

gamma_list : list

List of the regularization parameters in the path

Examples

--------

>>> import ot

>>> import numpy as np

>>> n = 3

>>> xs = np.array([1., 2., 3.]).reshape((n, 1))

>>> xt = np.array([5., 6., 7.]).reshape((n, 1))

>>> C = ot.dist(xs, xt)

>>> C /= C.max()

>>> a = np.array([0.2, 0.5, 0.3])

>>> b = np.array([0.2, 0.5, 0.3])

>>> t, _, _ = ot.regpath.semi_relaxed_path(a, b, C, 1e-4)

>>> t

array([1.99980556e-01, 0.00000000e+00, 0.00000000e+00, 1.94444444e-05,

4.99980556e-01, 0.00000000e+00, 0.00000000e+00, 1.94444444e-05,

3.00000000e-01])

References

----------

.. [41] Chapel, L., Flamary, R., Wu, H., Févotte, C., and Gasso, G. (2021).

Unbalanced optimal transport through non-negative penalized

linear regression. NeurIPS.

"""

Hr, Hc, c = recast_semi_relaxed_as_lasso(a, b, C)

Hra = Hr.T.dot(a)

HrHr = Hr.T.dot(Hr)

n_iter = 1

active_index = []

# initialization

for j in range(np.shape(C)[1]):

i = np.argmin(C[:, j])

active_index.append(i * m + j)

gamma_list = []

t_list = []

current_gamma = np.inf

augmented_H0 = construct_augmented_H(active_index, m, Hc, HrHr)

add_col = np.array([])

id_pop = -1

while n_iter < itmax and current_gamma > reg:

if n_iter == 1:

H_inv = np.linalg.inv(augmented_H0)

else:

H_inv = complement_schur(H_inv, add_col, 1.0, id_pop + m)

# compute the intercept and slope of solutions in current iteration

augmented_phi = H_inv.dot(np.concatenate((b, Hra[active_index])))

augmented_delta = H_inv[:, m:].dot(c[active_index])

phi = augmented_phi[m:]

delta = augmented_delta[m:]

phi_u = augmented_phi[0:m]

delta_u = augmented_delta[0:m]

gamma, ik = semi_relaxed_next_gamma(

)

# compute the next lambda when removing a point from the active set

# if the positivity constraint is violated, we remove id_pop

# from active set, otherwise we add ik to active set

if alt_gamma > gamma:

gamma = alt_gamma

else:

id_pop = -1

# compute the solution of current segment

tA = phi - gamma * delta

sol[active_index] = tA

if id_pop != -1:

active_index.pop(id_pop)

add_col = None

else:

active_index.append(ik)

add_col = np.concatenate(

(Hc.toarray()[:, ik], HrHr.toarray()[active_index[:-1], ik])

)

add_col = add_col[:, np.newaxis]

gamma_list.append(gamma)

t_list.append(sol)

current_gamma = gamma

n_iter += 1

if itmax <= n_iter:

# correct the last solution and gamma

)

t_list[-1] = t_final

gamma_list[-1] = reg

else:

gamma_list[-1] = reg

warnings.warn("Regularization path does not exist.")

def regularization_path(

a: np.array, b: np.array, C: np.array, reg=1e-4, semi_relaxed=False, itmax=50000

):

r"""This function provides all the solutions of the regularization path \

of the l2-UOT problem :ref:`[41] <references-regpath>`.

The problem to optimize is the Lasso reformulation of the l2-penalized UOT:

.. math::

\min_t \gamma \mathbf{c}^T \mathbf{t}

+ 0.5 * \|{H} \mathbf{t} - \mathbf{y}\|_2^2

s.t.

\mathbf{t} \geq 0

where :

- :math:`\mathbf{c}` is the flattened version of the cost matrix \

:math:`{C}`

- :math:`\gamma = 1/\lambda` is the l2-regularization coefficient

- :math:`\mathbf{y}` is the concatenation of vectors :math:`\mathbf{a}` \

and :math:`\mathbf{b}`, defined as \

:math:`\mathbf{y}^T = [\mathbf{a}^T \mathbf{b}^T]`

- :math:`{H}` is a design matrix, see :ref:`[41] <references-regpath>` \

for the design of :math:`{H}`. The matrix product :math:`H\mathbf{t}` \

computes both the source marginal and the target marginals.

- :math:`\mathbf{t}` is the flattened version of the transport matrix

For the semi-relaxed problem, it optimizes the Lasso reformulation of the

l2-penalized UOT:

.. math::

\min_t \gamma \mathbf{c}^T \mathbf{t}

+ 0.5 * \|H_r \mathbf{t} - \mathbf{a}\|_2^2

s.t.

H_c \mathbf{t} = \mathbf{b}

\mathbf{t} \geq 0

Parameters

----------

a : np.ndarray (dim_a,)

Histogram of dimension dim_a

b : np.ndarray (dim_b,)

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