sisl.mixing.AdaptivePulayMixer

class sisl.mixing.AdaptivePulayMixer(weight=(0.03, 0.5), history=2, metric=None)

Bases: sisl.mixing.AdaptiveDIISMixer

Methods

adjust_weight(lagrange[, offset, spread])

Adjust the weight according to the Lagrange multiplier.

append(*args[, variable])

Add variables to the history

clear([index, variables])

Clear variables to the history

coefficients()

Calculate coefficients and adjust weights according to a Lagrange multiplier

inner(a, b)

Perform the inner product between a and b

mix(coeff)

Calculate a new variable \(f'\) using history and input coefficients

solve_lagrange()

Calculate the coefficients according to Pulay's method, return everything + Lagrange multiplier

history

Number of elements in the history

history_max

Maximum number of elements stored in the history for each variable

variables

Number of different variables that can be contained

weight

Weight used for the linear mixing
__call__(f, df)

Call self as a function.

__init__(weight=(0.03, 0.5), history=2, metric=None)
adjust_weight(lagrange, offset=13, spread=7)

Adjust the weight according to the Lagrange multiplier.

Once close to convergence the Lagrange multiplier will be close to 0, otherwise it will go towards infinity. We here adjust using the Fermi-function to hit the minimum/maximum weight with a suitable spread

append(*args, variable=None)

Add variables to the history

Parameters

  • *args (tuple of object) – each variable will be added to the history of the mixer

  • variable (int or listlike of int) – specify which variables the history should be added to, note: len(args) == len(variable)

clear(index=None, variables=None)

Clear variables to the history

Parameters

  • index (int or array_like of int) – which indices of the history we should clear

  • variables (int or array_like of int) – specify which variables should be cleared

coefficients()

Calculate coefficients and adjust weights according to a Lagrange multiplier

property history

Number of elements in the history

property history_max

Maximum number of elements stored in the history for each variable

inner(a, b)

Perform the inner product between a and b

Parameters
mix(coeff)

Calculate a new variable \(f'\) using history and input coefficients

Parameters

coeff (numpy.ndarray) – coefficients used for extrapolation

solve_lagrange()

Calculate the coefficients according to Pulay’s method, return everything + Lagrange multiplier

property variables

Number of different variables that can be contained

property weight

Weight used for the linear mixing