Source code for triqs.utility.dichotomy

# Copyright (c) 2013 Commissariat à l'énergie atomique et aux énergies alternatives (CEA)
# Copyright (c) 2013 Centre national de la recherche scientifique (CNRS)
# Copyright (c) 2019-2020 Simons Foundation
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You may obtain a copy of the License at
# Authors: Manuel, Olivier Parcollet, Hugo U. R. Strand, Nils Wentzell

import triqs.utility.mpi as mpi
import numpy as np

[docs] def dichotomy(function, x_init, y_value, precision_on_y, delta_x, max_loops = 1000, x_name="", y_name="", verbosity=1): r""" Finds :math:`x` that solves :math:`y = f(x)`. Starting at ``x_init``, which is used as the lower upper/bound, dichotomy finds first the upper/lower bound by adding/subtracting ``delta_x``. Then bisection is used to refine :math:`x` until ``abs(f(x) - y_value) < precision_on_y`` or ``max_loops`` is reached. Parameters ---------- function : function, real valued Function :math:`f(x)`. It must take only one real parameter. x_init : double Initial guess for x. On success, returns the new value of x. y_value : double Target value for y. precision_on_y : double Stops if ``abs(f(x) - y_value) < precision_on_y``. delta_x : double :math:`\Delta x` added/subtracted from ``x_init`` until the second bound is found. max_loops : integer, optional Maximum number of loops (default is 1000). x_name : string, optional Name of variable x used for printing. y_name : string, optional Name of variable y used for printing. verbosity : integer, optional Verbosity level. Returns ------- (x,y) : (double, double) :math:`x` and :math:`y=f(x)`. Returns (None, None) if dichotomy failed. """"Dichotomy adjustment of %(x_name)s to obtain %(y_name)s = %(y_value)f +/- %(precision_on_y)f"%locals() ) PR = " " if x_name == "" or y_name == "" : verbosity = max(verbosity,1) x=x_init;delta_x= abs(delta_x) # First find the bounds y1 = function(x) eps = np.sign(y1-y_value) x1=x;y2=y1;x2=x1 nbre_loop=0 while (nbre_loop<= max_loops) and (y2-y_value)*eps>0 and abs(y2-y_value)>precision_on_y : nbre_loop +=1 x2 -= eps*delta_x y2 = function(x2) if x_name!="" and verbosity>2:"%(PR)s%(x_name)s = %(x2)f \n%(PR)s%(y_name)s = %(y2)f"%locals()) # Make sure that x2 > x1 if x1 > x2: x1,x2 = x2,x1 y1,y2 = y2,y1"%(PR)s%(x1)f < %(x_name)s < %(x2)f"%locals())"%(PR)s%(y1)f < %(y_name)s < %(y2)f"%locals()) # We found bounds. # If one of the two bounds is already close to the solution # the bisection will not run. For this case we set x and yfound. if abs(y1-y_value) < abs(y2-y_value) : yfound = y1 x = x1 else: yfound = y2 x = x2 #Now let's refine between the bounds while (nbre_loop<= max_loops) and (abs(yfound-y_value)>precision_on_y) : nbre_loop +=1 x = x1 + (x2 - x1) * (y_value - y1)/(y2-y1) yfound = function(x) if (y1-y_value)*(yfound - y_value)>0 : x1 = x; y1=yfound else : x2= x;y2=yfound; if verbosity > 2:"%(PR)s%(x1)f < %(x_name)s < %(x2)f"%locals())"%(PR)s%(y1)f < %(y_name)s < %(y2)f"%locals()) if abs(yfound - y_value) < precision_on_y : if verbosity>0:"%(PR)s%(x_name)s found in %(nbre_loop)d iterations : "%locals())"%(PR)s%(y_name)s = %(yfound)f;%(x_name)s = %(x)f"%locals()) return (x,yfound) else : if verbosity > 0:"%(PR)sFAILURE to adjust %(x_name)s to the value %(y_value)f after %(nbre_loop)d iterations."%locals())"%(PR)sFAILURE returning (None, None) due to failure."%locals()) return (None,None)