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3.12 Optimization and root finding (scipy.optimize)
3.12.1 Optimization
General-purpose
SciPy Reference Guide, Release 0.8.dev
fmin(func, x0[, args, xtol, ftol, maxiter, ...]) Minimize a function using the downhill simplex algorithm.
fmin_powell(func, x0[, args, xtol, ftol, ...]) Minimize a function using modified Powell’s method.
fmin_cg(f, x0[, fprime, args, gtol, norm, ...]) Minimize a function using a nonlinear conjugate gradient
algorithm.
fmin_bfgs(f, x0[, fprime, args, gtol, norm,
...])
Minimize a function using the BFGS algorithm.
fmin_ncg(f, x0, fprime[, fhess_p, fhess, ...]) Minimize a function using the Newton-CG method.
leastsq(func, x0[, args, Dfun, full_output,
...])
Minimize the sum of squares of a set of equations.
fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0,
callback=None)
Minimize a function using the downhill simplex algorithm.
Parameters
func
[callable func(x,*args)] The objective function to be minimized.
x0
[ndarray] Initial guess.
args
[tuple] Extra arguments passed to func, i.e. f(x,*args).
callback
[callable] Called after each iteration, as callback(xk), where xk is the current parameter
vector.
Returns
(xopt, {fopt, iter, funcalls, warnflag})
Other Parameters:
xopt
[ndarray] Parameter that minimizes function.
fopt
[float] Value of function at minimum: fopt = func(xopt).
iter
[int] Number of iterations performed.
funcalls
[int] Number of function calls made.
warnflag
[int] 1 : Maximum number of function evaluations made. 2 : Maximum number of
iterations reached.
allvecs
[list] Solution at each iteration.
3.12. Optimization and root finding (scipy.optimize) 297