changes forgotten coments

1 files changed, 16 insertions, 16 deletions

diff --git a/ot/optim.py b/ot/optim.py
index 4d428d9..f94aceb 100644
--- a/ot/optim.py
+++ b/ot/optim.py
@@ -73,8 +73,8 @@ def line_search_armijo(f, xk, pk, gfk, old_fval,
     return alpha, fc[0], phi1
 
 
-def do_linesearch(cost, G, deltaG, Mi, f_val,
-                  armijo=True, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None):
+def solve_linesearch(cost, G, deltaG, Mi, f_val,
+                     armijo=True, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None):
     """
     Solve the linesearch in the FW iterations
     Parameters
@@ -93,17 +93,17 @@ def do_linesearch(cost, G, deltaG, Mi, f_val,
             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 : ndarray (ns,ns), optional
-        Structure matrix in the source domain. Only used when armijo=False
+        Structure matrix in the source domain. Only used and necessary when armijo=False
     C2 : ndarray (nt,nt), optional
-        Structure matrix in the target domain. Only used when armijo=False
+        Structure matrix in the target domain. Only used and necessary when armijo=False
     reg : float, optional
-          Regularization parameter. Only used when armijo=False
+          Regularization parameter. Only used and necessary when armijo=False
     Gc : ndarray (ns,nt)
-        Optimal map found by linearization in the FW algorithm. Only used when armijo=False
+        Optimal map found by linearization in the FW algorithm. Only used and necessary when armijo=False
     constC : ndarray (ns,nt)
-             Constant for the gromov cost. See [24]. Only used when armijo=False
+             Constant for the gromov cost. See [24]. Only used and necessary when armijo=False
     M : ndarray (ns,nt), optional
-        Cost matrix between the features. Only used when armijo=False
+        Cost matrix between the features. Only used and necessary when armijo=False
     Returns
     -------
     alpha : float
@@ -128,7 +128,7 @@ def do_linesearch(cost, G, deltaG, Mi, f_val,
         b = np.sum((M + reg * constC) * deltaG) - 2 * reg * (np.sum(dot12 * G) + np.sum(np.dot(C1, G).dot(C2) * deltaG))
         c = cost(G)
 
-        alpha = solve_1d_linesearch_quad_funct(a, b, c)
+        alpha = solve_1d_linesearch_quad(a, b, c)
         fc = None
         f_val = cost(G + alpha * deltaG)
 
@@ -181,7 +181,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
         Print information along iterations
     log : bool, optional
         record log if True
-    kwargs : dict
+    **kwargs : dict
              Parameters for linesearch
 
     Returns
@@ -244,7 +244,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
         deltaG = Gc - G
 
         # line search
-        alpha, fc, f_val = do_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc, **kwargs)
+        alpha, fc, f_val = solve_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc, **kwargs)
 
         G = G + alpha * deltaG
 
@@ -254,7 +254,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
 
         abs_delta_fval = abs(f_val - old_fval)
         relative_delta_fval = abs_delta_fval / abs(f_val)
-        if relative_delta_fval < stopThr and abs_delta_fval < stopThr2:
+        if relative_delta_fval < stopThr or abs_delta_fval < stopThr2:
             loop = 0
 
         if log:
@@ -395,7 +395,7 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10,
         abs_delta_fval = abs(f_val - old_fval)
         relative_delta_fval = abs_delta_fval / abs(f_val)
 
-        if relative_delta_fval < stopThr and abs_delta_fval < stopThr2:
+        if relative_delta_fval < stopThr or abs_delta_fval < stopThr2:
             loop = 0
 
         if log:
@@ -413,11 +413,11 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10,
         return G
 
 
-def solve_1d_linesearch_quad_funct(a, b, c):
+def solve_1d_linesearch_quad(a, b, c):
     """
-    Solve on 0,1 the following problem:
+    For any convex or non-convex 1d quadratic function f, solve on [0,1] the following problem:
     .. math::
-        \min f(x)=a*x^{2}+b*x+c
+        \argmin f(x)=a*x^{2}+b*x+c
 
     Parameters
     ----------