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Diffstat (limited to 'docs/source/auto_examples/plot_optim_OTreg.ipynb')
| -rw-r--r-- | docs/source/auto_examples/plot_optim_OTreg.ipynb | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index 9d26e4d..290100f 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# Regularized OT with generic solver\n\n\n\n\n" + "\n# Regularized OT with generic solver\n\n\nIllustrates the use of the generic solver for regularized OT with\nuser-designed regularization term. It uses Conditional gradient as in [6] and \ngeneralized Conditional Gradient as proposed in [5][7].\n\n\n[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for \nDomain Adaptation, in IEEE Transactions on Pattern Analysis and Machine \nIntelligence , vol.PP, no.99, pp.1-1.\n\n[6] Ferradans, S., Papadakis, N., Peyr\u00e9, G., & Aujol, J. F. (2014). \nRegularized discrete optimal transport. SIAM Journal on Imaging Sciences, \n7(3), 1853-1882.\n\n[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized \nconditional gradient: analysis of convergence and applications. \narXiv preprint arXiv:1510.06567.\n\n\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data \n#############################################################################\n\n" + "Generate data\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Solve EMD \n#############################################################################\n\n" + "Solve EMD\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -114,7 +114,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "#%% Example with Frobenius norm + entropic regularization with gcg\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" + "#%% Example with Frobenius norm + entropic regularization with gcg\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" ], "outputs": [], "metadata": { |