diff options
| -rw-r--r-- | paper/sections/results.tex | 3 | ||||
| -rw-r--r-- | src/convex_optimization.py | 2 | ||||
| -rw-r--r-- | src/make_plots.py | 6 |
3 files changed, 5 insertions, 6 deletions
diff --git a/paper/sections/results.tex b/paper/sections/results.tex index 46521ed..95a0826 100644 --- a/paper/sections/results.tex +++ b/paper/sections/results.tex @@ -164,8 +164,7 @@ Choosing $\lambda\defeq 2\sqrt{\frac{\log m}{\alpha n^{1-\delta}}}$ concludes th proof. \end{proof} -Note how the proof of Lemma 3 relied crucially on Azuma-Hoeffding's inequality: by supposing ... We now show how to use Theorem~\ref{thm:main} to recover the support of -$\theta^*$, that is, to solve the Graph Inference problem. +Note how the proof of Lemma~\ref{lem:ub} relied crucially on Azuma-Hoeffding's inequality, which allows us to handle correlated observations, and obtain bounds on the number of measurements rather than the number of cascades. We now show how to use Theorem~\ref{thm:main} to recover the support of $\theta^*$, that is, to solve the Graph Inference problem. \begin{corollary} \label{cor:variable_selection} diff --git a/src/convex_optimization.py b/src/convex_optimization.py index 0d506e1..8dc6f82 100644 --- a/src/convex_optimization.py +++ b/src/convex_optimization.py @@ -90,7 +90,7 @@ def diff_and_opt(M_val, w_val, f_x, f_xz): def F(x=None, z=None): if x is None: - return 0, cvxopt.matrix(-.1, (n,1)) + return 0, cvxopt.matrix(-.001, (n,1)) elif z is None: y, y_diff = f_x(x, M_val, w_val) return cvxopt.matrix(float(y), (1, 1)),\ diff --git a/src/make_plots.py b/src/make_plots.py index e9e0de3..81d581c 100644 --- a/src/make_plots.py +++ b/src/make_plots.py @@ -168,8 +168,8 @@ if __name__=="__main__": #convex_optimization.type_lasso) if 1: compute_graph("../datasets/kronecker_graph_256_cross.txt", - n_cascades=2000, lbda=0., min_proba=.2, max_proba=.7, + n_cascades=2000, lbda=0.1, min_proba=.2, max_proba=.7, passed_function= - convex_optimization.sparse_recovery, - #convex_optimization.type_lasso, + #convex_optimization.sparse_recovery, + convex_optimization.type_lasso, sparse_edges=True)
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