@@ -136,7 +136,7 @@ def solve_linesearch(cost, G, deltaG, Mi, f_val,
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def cg (a , b , M , reg , f , df , G0 = None , numItermax = 200 , numItermaxEmd = 100000 ,
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stopThr = 1e-9 , stopThr2 = 1e-9 , verbose = False , log = False , ** kwargs ):
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- r """
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+ """
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Solve the general regularized OT problem with conditional gradient
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The function solves the following optimization problem:
@@ -275,7 +275,7 @@ def cost(G):
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def gcg (a , b , M , reg1 , reg2 , f , df , G0 = None , numItermax = 10 ,
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numInnerItermax = 200 , stopThr = 1e-9 , stopThr2 = 1e-9 , verbose = False , log = False ):
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- r """
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+ """
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Solve the general regularized OT problem with the generalized conditional gradient
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The function solves the following optimization problem:
@@ -413,7 +413,7 @@ def cost(G):
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def solve_1d_linesearch_quad (a , b , c ):
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- r """
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+ """
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For any convex or non-convex 1d quadratic function f, solve on [0,1] the following problem:
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.. math::
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\a rgmin f(x)=a*x^{2}+b*x+c
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