scipy.optimize.

fmin_cobyla#

scipy.optimize.fmin_cobyla(func, x0, cons, args=(), consargs=None, rhobeg=1.0, rhoend=0.0001, maxfun=1000, disp=None, catol=0.0002, *, callback=None)[source]#

Minimize a function using the Constrained Optimization By Linear Approximation (COBYLA) method. This method wraps a FORTRAN implementation of the algorithm.

Parameters:

funccallable: Function to minimize. In the form func(x, *args).
x0ndarray: Initial guess.
conssequence: Constraint functions; must all be >=0 (a single function if only 1 constraint). Each function takes the parameters x as its first argument, and it can return either a single number or an array or list of numbers.
argstuple, optional: Extra arguments to pass to function.
consargstuple, optional: Extra arguments to pass to constraint functions (default of None means use same extra arguments as those passed to func). Use () for no extra arguments.
rhobegfloat, optional: Reasonable initial changes to the variables.
rhoendfloat, optional: Final accuracy in the optimization (not precisely guaranteed). This is a lower bound on the size of the trust region.
disp{0, 1, 2, 3}, optional: Controls the frequency of output; 0 implies no output.
maxfunint, optional: Maximum number of function evaluations.
catolfloat, optional: Absolute tolerance for constraint violations.
callbackcallable, optional: Called after each iteration, as callback(x), where x is the current parameter vector.

Returns:

xndarray: The argument that minimises f.

See also

minimize: Interface to minimization algorithms for multivariate functions. See the ‘COBYLA’ method in particular.

Notes

This algorithm is based on linear approximations to the objective function and each constraint. We briefly describe the algorithm.

Suppose the function is being minimized over k variables. At the jth iteration the algorithm has k+1 points v_1, …, v_(k+1), an approximate solution x_j, and a radius RHO_j. (i.e., linear plus a constant) approximations to the objective function and constraint functions such that their function values agree with the linear approximation on the k+1 points v_1,.., v_(k+1). This gives a linear program to solve (where the linear approximations of the constraint functions are constrained to be non-negative).

However, the linear approximations are likely only good approximations near the current simplex, so the linear program is given the further requirement that the solution, which will become x_(j+1), must be within RHO_j from x_j. RHO_j only decreases, never increases. The initial RHO_j is rhobeg and the final RHO_j is rhoend. In this way COBYLA’s iterations behave like a trust region algorithm.

Additionally, the linear program may be inconsistent, or the approximation may give poor improvement. For details about how these issues are resolved, as well as how the points v_i are updated, refer to the source code or the references below.

References

Powell M.J.D. (1994), “A direct search optimization method that models the objective and constraint functions by linear interpolation.”, in Advances in Optimization and Numerical Analysis, eds. S. Gomez and J-P Hennart, Kluwer Academic (Dordrecht), pp. 51-67

Powell M.J.D. (1998), “Direct search algorithms for optimization calculations”, Acta Numerica 7, 287-336

Powell M.J.D. (2007), “A view of algorithms for optimization without derivatives”, Cambridge University Technical Report DAMTP 2007/NA03

Examples

Minimize the objective function f(x,y) = x*y subject to the constraints x**2 + y**2 < 1 and y > 0:

>>> def objective(x):
...     return x[0]*x[1]
...
>>> def constr1(x):
...     return 1 - (x[0]**2 + x[1]**2)
...
>>> def constr2(x):
...     return x[1]
...
>>> from scipy.optimize import fmin_cobyla
>>> fmin_cobyla(objective, [0.0, 0.1], [constr1, constr2], rhoend=1e-7)
array([-0.70710685,  0.70710671])

The exact solution is (-sqrt(2)/2, sqrt(2)/2).