asd#

asd(function, x, args=None, stepsize=0.1, sinc=2, sdec=2, pinc=2, pdec=2, pinitial=None, sinitial=None, xmin=None, xmax=None, maxiters=None, maxtime=None, abstol=1e-06, reltol=0.001, stalliters=None, stoppingfunc=None, randseed=None, label=None, verbose=1, minval=0, **kwargs)[source]#

Optimization using adaptive stochastic descent (ASD). Can be used as a faster and more powerful alternative to e.g. scipy.optimize.minimize().

ASD starts at x0 and attempts to find a local minimizer x of the function func(). func() accepts input x and returns a scalar function value evaluated at x. x0 can be a scalar, list, or Numpy array of any size.

Parameters:

  • function (func) – The function to minimize

  • x (arr) – The vector of initial parameters

  • args (any) – List, tuple, or dictionary of additional parameters to be passed to the function

  • kwargs (dict) – Additional keywords passed to the function

  • stepsize (0.1) – Initial step size as a fraction of each parameter

  • sinc (2) – Step size learning rate (increase)

  • sdec (2) – Step size learning rate (decrease)

  • pinc (2) – Parameter selection learning rate (increase)

  • pdec (2) – Parameter selection learning rate (decrease)

  • pinitial (None) – Set initial parameter selection probabilities

  • sinitial (None) – Set initial step sizes; if empty, calculated from stepsize instead

  • xmin (None) – Min value allowed for each parameter

  • xmax (None) – Max value allowed for each parameter

  • maxiters (1000) – Maximum number of iterations (1 iteration = 1 function evaluation)

  • maxtime (3600) – Maximum time allowed, in seconds

  • abstol (1e-6) – Minimum absolute change in objective function

  • reltol (1e-3) – Minimum relative change in objective function

  • stalliters (10*n) – Number of iterations over which to calculate TolFun (n = number of parameters)

  • stoppingfunc (None) – External method that can be used to stop the calculation from the outside.

  • randseed (None) – The random seed to use

  • label (None) – A label to use to annotate the output

  • verbose (1) – How much information to print during the run (max 3); less than one will print out once every 1/verbose steps

  • minval (0) – Minimum value the objective function can take

Returns:

objdict (see below)

The returned object is an objdict, which can be accessed by index, key, or attribute. Its keys/attributes are:

  • x – The parameter set that minimizes the objective function

  • fval – The value of the objective function at the final iteration

  • exitreason – Why the algorithm terminated;

  • details – See below

The details key consists of:

  • fvals – The value of the objective function at each iteration

  • xvals – The parameter values at each iteration;

  • probabilities – The probability of each step; and

  • stepsizes – The size of each step for each parameter.

Examples:

# Basic usage
import numpy as np
import sciris as sc
result = sc.asd(np.linalg.norm, [1, 2, 3])
print(result.x)

# With arguments
def my_func(x, scale=1.0, weight=1.0): # Example function with keywords
    return abs((x[0] - 1)) + abs(x[1] + 2)*scale + abs(x[2] + 3)*weight

result = sc.asd(my_func, x=[0, 0, 1], args=[0.5, 0.1]) # Option 1 for passing arguments
result = sc.asd(my_func, x=[0, 0, 1], args=dict(scale=0.5, weight=0.1)) # Option 1 for passing arguments
result = sc.asd(my_func, x=[0, 0, 1], scale=0.5, weight=0.1) # Option 2 for passing arguments

Please use the following citation for this method:

CC Kerr, S Dura-Bernal, TG Smolinski, GL Chadderdon, DP Wilson (2018). Optimization by adaptive stochastic descent. PLOS ONE 13 (3), e0192944.

New in version 3.0.0: Uses its own random number stream
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