add problems solved in doc

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diff --git a/test/test_stochastic.py b/test/test_stochastic.py
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+"""
+==========================
+Stochastic test
+==========================
+
+This example is designed to test the stochatic optimization algorithms module
+for descrete and semicontinous measures from the POT library.
+
+"""
+
+# Author: Kilian Fatras <kilian.fatras@gmail.com>
+#
+# License: MIT License
+
+import numpy as np
+import ot
+
+#############################################################################
+#
+# TEST SAG algorithm
+# ---------------------------------------------
+# 2 identical discrete measures u defined on the same space with a
+# regularization term, a learning rate and a number of iteration
+
+
+def test_stochastic_sag():
+    # test sag
+    n = 15
+    reg = 1
+    numItermax = 300000
+    rng = np.random.RandomState(0)
+
+    x = rng.randn(n, 2)
+    u = ot.utils.unif(n)
+
+    M = ot.dist(x, x)
+
+    G = ot.stochastic.transportation_matrix_entropic(u, u, M, reg, "sag",
+                                                     numItermax=numItermax)
+
+    # check constratints
+    np.testing.assert_allclose(
+        u, G.sum(1), atol=1e-04)  # cf convergence sag
+    np.testing.assert_allclose(
+        u, G.sum(0), atol=1e-04)  # cf convergence sag
+
+
+#############################################################################
+#
+# TEST ASGD algorithm
+# ---------------------------------------------
+# 2 identical discrete measures u defined on the same space with a
+# regularization term, a learning rate and a number of iteration
+
+def test_stochastic_asgd():
+    # test asgd
+    n = 15
+    reg = 1
+    numItermax = 300000
+    lr = 1
+    rng = np.random.RandomState(0)
+
+    x = rng.randn(n, 2)
+    u = ot.utils.unif(n)
+
+    M = ot.dist(x, x)
+
+    G = ot.stochastic.transportation_matrix_entropic(u, u, M, reg, "asgd",
+                                                     numItermax=numItermax,
+                                                     lr=lr)
+
+    # check constratints
+    np.testing.assert_allclose(
+        u, G.sum(1), atol=1e-03)  # cf convergence asgd
+    np.testing.assert_allclose(
+        u, G.sum(0), atol=1e-03)  # cf convergence asgd
+
+
+#############################################################################
+#
+# TEST Convergence SAG and ASGD toward Sinkhorn's solution
+# --------------------------------------------------------
+# 2 identical discrete measures u defined on the same space with a
+# regularization term, a learning rate and a number of iteration
+
+
+def test_sag_asgd_sinkhorn():
+    # test all algorithms
+    n = 15
+    reg = 1
+    nb_iter = 300000
+    lr = 1
+    rng = np.random.RandomState(0)
+
+    x = rng.randn(n, 2)
+    u = ot.utils.unif(n)
+    zero = np.zeros(n)
+    M = ot.dist(x, x)
+
+    G_asgd = ot.stochastic.transportation_matrix_entropic(u, u, M, reg, "asgd",
+                                                          numItermax=nb_iter,
+                                                          lr=1)
+    G_sag = ot.stochastic.transportation_matrix_entropic(u, u, M, reg, "sag",
+                                                         numItermax=nb_iter)
+    G_sinkhorn = ot.sinkhorn(u, u, M, reg)
+
+    # check constratints
+    np.testing.assert_allclose(
+        zero, (G_sag - G_sinkhorn).sum(1), atol=1e-03)  # cf convergence sag
+    np.testing.assert_allclose(
+        zero, (G_sag - G_sinkhorn).sum(0), atol=1e-03)  # cf convergence sag
+    np.testing.assert_allclose(
+        zero, (G_asgd - G_sinkhorn).sum(1), atol=1e-03)  # cf convergence asgd
+    np.testing.assert_allclose(
+        zero, (G_asgd - G_sinkhorn).sum(0), atol=1e-03)  # cf convergence asgd