Optimization (minimization) functions

Module Contents


minimize(fn, x0[, method, callback, tol, maxiter, ...])

Minimizes the function fn starting at x0.

create_objfn_printer(obj_func[, start_time])

Create a callback function that prints the value of an objective function.

check_jac(f, x0, jac_to_check[, eps, tol, err_type, ...])

Checks a jacobian function using finite differences.

pygsti.optimize.optimize.minimize(fn, x0, method='cg', callback=None, tol=1e-10, maxiter=1000000, maxfev=None, stopval=None, jac=None, verbosity=0, **addl_kwargs)

Minimizes the function fn starting at x0.

This is a gateway function to all other minimization routines within this module, providing a common interface to many different minimization methods (including and extending beyond those available from scipy.optimize).



The function to minimize.

x0numpy array

The starting point (argument to fn).

methodstring, optional

Which minimization method to use. Allowed values are: “simplex” : uses _fmin_simplex “supersimplex” : uses _fmin_supersimplex “customcg” : uses fmax_cg (custom CG method) “brute” : uses scipy.optimize.brute “basinhopping” : uses scipy.optimize.basinhopping with L-BFGS-B “swarm” : uses _fmin_particle_swarm “evolve” : uses _fmin_evolutionary (which uses DEAP) < methods available from scipy.optimize.minimize >

callbackfunction, optional

A callback function to be called in order to track optimizer progress. Should have signature: myCallback(x, f=None, accepted=None). Note that create_objfn_printer(…) function can be used to create a callback.

tolfloat, optional

Tolerance value used for all types of tolerances available in a given method.

maxiterint, optional

Maximum iterations.

maxfevint, optional

Maximum function evaluations; used only when available, and defaults to maxiter.

stopvalfloat, optional

For basinhopping method only. When f <= stopval then basinhopping outer loop will terminate. Useful when a bound on the minimum is known.


Jacobian function.


Level of detail to print to stdout.


Additional arguments for the specific optimizer being used.


scipy.optimize.Result object

Includes members ‘x’, ‘fun’, ‘success’, and ‘message’.

pygsti.optimize.optimize.create_objfn_printer(obj_func, start_time=None)

Create a callback function that prints the value of an objective function.



The objective function to print.

start_timefloat , optional

A reference starting time to use when printing elapsed times. If None, then the system time when this function is called is used (which is often what you want).



A callback function which prints obj_func.

pygsti.optimize.optimize.check_jac(f, x0, jac_to_check, eps=1e-10, tol=1e-06, err_type='rel', verbosity=1)

Checks a jacobian function using finite differences.



The function to check.

x0numpy array

The point at which to check the jacobian.


A function which should compute the jacobian of f at x0.

epsfloat, optional

Epsilon to use in finite difference calculations of jacobian.

tolfloat, optional

The allowd tolerance on the relative differene between the values of the finite difference and jac_to_check jacobians if err_type == ‘rel’ or the absolute difference if err_type == ‘abs’.

err_type{‘rel’, ‘abs’), optional

How to interpret tol (see above).

verbosityint, optional

Controls how much detail is printed to stdout.



The total error between the jacobians.


List of (row,col,err) tuples giving the error for each row and column.

ffd_jacnumpy array

The computed forward-finite-difference jacobian.