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| author | Mokhtar Z. Alaya <mzalaya@sct-18-1bee-1581.univ-rouen.fr> | 2020-01-10 12:00:08 +0100 |
|---|---|---|
| committer | Mokhtar Z. Alaya <mzalaya@sct-18-1bee-1581.univ-rouen.fr> | 2020-01-10 12:00:08 +0100 |
| commit | cadd301c4de54332378159ab55a02a48beb1d753 (patch) | |
| tree | 2883d3c486f0b28a4ecc226441e9e8d239efe22e /ot/bregman.py | |
| parent | 5a70afeaa1671e4af010009d47bdea1073967e1e (diff) | |
improve doc
Diffstat (limited to 'ot/bregman.py')
| -rw-r--r-- | ot/bregman.py | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/ot/bregman.py b/ot/bregman.py index 95b27e4..0f02226 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1799,15 +1799,15 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res The function solves an approximate dual of Sinkhorn divergence [2] which is written as the following optimization problem: ..math:: - (u, v) = \argmin_{u, v} 1_{ns}^\top B(u,v) 1_{nt} - <\kappa u, a> - <v/\kappa, b> + (u, v) = \argmin_{u, v} 1_{ns}^T B(u,v) 1_{nt} - <\kappa u, a> - <v/\kappa, b> - where B(u,v) = \diag(e^u) K \diag(e^v), with K = e^{-M/reg} and + where B(u,v) = \diag(e^u) K \diag(e^v), with K = e^{-M/reg} and - s.t. e^{u_i} >= \epsilon / \kappa, for all i in {1, ..., ns} + s.t. e^{u_i} \geq \epsilon / \kappa, for all i \in {1, ..., ns} - e^{v_j} >= \epsilon \kappa, for all j in {1, ..., nt} + e^{v_j} \geq \epsilon \kappa, for all j \in {1, ..., nt} - The parameters \kappa and \epsilon are determined w.r.t the couple number budget of points (ns_budget, nt_budget), see Equation (5) in [26] + The parameters \kappa and \epsilon are determined w.r.t the couple number budget of points (ns_budget, nt_budget), see Equation (5) in [26] Parameters @@ -1843,9 +1843,9 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res Maximum number of iterations in LBFGS solver maxfun : `int`, default=10000 - Maximum number of function evaluations in LBFGS solver + Maximum number of function evaluations in LBFGS solver - pgtol : `float`, default=1e-09 + pgtol : `float`, default=1e-09 Final objective function accuracy in LBFGS solver verbose: `bool`, default=False @@ -2116,4 +2116,4 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res if log: return gamma, log else: - return gamma
\ No newline at end of file + return gamma |