diff options
Diffstat (limited to 'ot/optim.py')
| -rw-r--r-- | ot/optim.py | 47 |
1 files changed, 34 insertions, 13 deletions
diff --git a/ot/optim.py b/ot/optim.py index bd8ca26..f25e2c9 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -18,8 +18,10 @@ from .backend import get_backend # The corresponding scipy function does not work for matrices -def line_search_armijo(f, xk, pk, gfk, old_fval, - args=(), c1=1e-4, alpha0=0.99): +def line_search_armijo( + f, xk, pk, gfk, old_fval, args=(), c1=1e-4, + alpha0=0.99, alpha_min=None, alpha_max=None +): r""" Armijo linesearch function that works with matrices @@ -44,6 +46,10 @@ def line_search_armijo(f, xk, pk, gfk, old_fval, :math:`c_1` const in armijo rule (>0) alpha0 : float, optional initial step (>0) + alpha_min : float, optional + minimum value for alpha + alpha_max : float, optional + maximum value for alpha Returns ------- @@ -77,14 +83,18 @@ def line_search_armijo(f, xk, pk, gfk, old_fval, alpha, phi1 = scalar_search_armijo( phi, phi0, derphi0, c1=c1, alpha0=alpha0) - # scalar_search_armijo can return alpha > 1 - if alpha is not None: - alpha = min(1, alpha) - return alpha, fc[0], phi1 + if alpha is None: + return 0., fc[0], phi0 + else: + if alpha_min is not None or alpha_max is not None: + alpha = np.clip(alpha, alpha_min, alpha_max) + return float(alpha), fc[0], phi1 -def solve_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, alpha_min=None, alpha_max=None +): """ Solve the linesearch in the FW iterations @@ -115,6 +125,10 @@ def solve_linesearch(cost, G, deltaG, Mi, f_val, Constant for the gromov cost. See :ref:`[24] <references-solve-linesearch>`. Only used and necessary when armijo=False M : array-like (ns,nt), optional Cost matrix between the features. Only used and necessary when armijo=False + alpha_min : float, optional + Minimum value for alpha + alpha_max : float, optional + Maximum value for alpha Returns ------- @@ -134,7 +148,9 @@ def solve_linesearch(cost, G, deltaG, Mi, f_val, International Conference on Machine Learning (ICML). 2019. """ if armijo: - alpha, fc, f_val = line_search_armijo(cost, G, deltaG, Mi, f_val) + alpha, fc, f_val = line_search_armijo( + cost, G, deltaG, Mi, f_val, alpha_min=alpha_min, alpha_max=alpha_max + ) else: # requires symetric matrices G, deltaG, C1, C2, constC, M = list_to_array(G, deltaG, C1, C2, constC, M) if isinstance(M, int) or isinstance(M, float): @@ -148,6 +164,8 @@ def solve_linesearch(cost, G, deltaG, Mi, f_val, c = cost(G) alpha = solve_1d_linesearch_quad(a, b, c) + if alpha_min is not None or alpha_max is not None: + alpha = np.clip(alpha, alpha_min, alpha_max) fc = None f_val = cost(G + alpha * deltaG) @@ -272,9 +290,10 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, numItermaxEmd=100000, deltaG = Gc - G # line search - alpha, fc, f_val = solve_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc, **kwargs) - if alpha is None: - alpha = 0.0 + alpha, fc, f_val = solve_linesearch( + cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc, + alpha_min=0., alpha_max=1., **kwargs + ) G = G + alpha * deltaG @@ -420,7 +439,9 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, # line search dcost = Mi + reg1 * (1 + nx.log(G)) # ?? - alpha, fc, f_val = line_search_armijo(cost, G, deltaG, dcost, f_val) + alpha, fc, f_val = line_search_armijo( + cost, G, deltaG, dcost, f_val, alpha_min=0., alpha_max=1. + ) G = G + alpha * deltaG |