# scipy.optimize.leastsq¶

scipy.optimize.leastsq(func, x0, args=(), Dfun=None, full_output=0, col_deriv=0, ftol=1.49012e-08, xtol=1.49012e-08, gtol=0.0, maxfev=0, epsfcn=0.0, factor=100, diag=None, warning=True)

Minimize the sum of squares of a set of equations.

Description:

Return the point which minimizes the sum of squares of M (non-linear) equations in N unknowns given a starting estimate, x0, using a modification of the Levenberg-Marquardt algorithm.

x = arg min(sum(func(y)**2,axis=0))
y

Inputs:

func – A Python function or method which takes at least one
(possibly length N vector) argument and returns M floating point numbers.

x0 – The starting estimate for the minimization. args – Any extra arguments to func are placed in this tuple. Dfun – A function or method to compute the Jacobian of func with

derivatives across the rows. If this is None, the Jacobian will be estimated.

full_output – non-zero to return all optional outputs. col_deriv – non-zero to specify that the Jacobian function

computes derivatives down the columns (faster, because there is no transpose operation).
warning – True to print a warning message when the call is
unsuccessful; False to suppress the warning message.

Outputs: (x, {cov_x, infodict, mesg}, ier)

x – the solution (or the result of the last iteration for an
unsuccessful call.
cov_x – uses the fjac and ipvt optional outputs to construct an
estimate of the covariance matrix of the solution. None if a singular matrix encountered (indicates infinite covariance in some direction).
infodict – a dictionary of optional outputs with the keys:

‘nfev’ : the number of function calls ‘fvec’ : the function evaluated at the output ‘fjac’ : A permutation of the R matrix of a QR

factorization of the final approximate Jacobian matrix, stored column wise. Together with ipvt, the covariance of the estimate can be approximated.
‘ipvt’ : an integer array of length N which defines
a permutation matrix, p, such that fjac*p = q*r, where r is upper triangular with diagonal elements of nonincreasing magnitude. Column j of p is column ipvt(j) of the identity matrix.

‘qtf’ : the vector (transpose(q) * fvec).

mesg – a string message giving information about the cause of failure. ier – an integer flag. If it is equal to 1, 2, 3 or 4, the

solution was found. Otherwise, the solution was not found. In either case, the optional output variable ‘mesg’ gives more information.

Extended Inputs:

ftol – Relative error desired in the sum of squares. xtol – Relative error desired in the approximate solution. gtol – Orthogonality desired between the function vector

and the columns of the Jacobian.
maxfev – The maximum number of calls to the function. If zero,
then 100*(N+1) is the maximum where N is the number of elements in x0.
epsfcn – A suitable step length for the forward-difference
approximation of the Jacobian (for Dfun=None). If epsfcn is less than the machine precision, it is assumed that the relative errors in the functions are of the order of the machine precision.
factor – A parameter determining the initial step bound
(factor * || diag * x||). Should be in interval (0.1,100).
diag – A sequency of N positive entries that serve as a
scale factors for the variables.

Remarks:

“leastsq” is a wrapper around MINPACK’s lmdif and lmder algorithms.

See also:

scikits.openopt, which offers a unified syntax to call this and other solvers

fmin, fmin_powell, fmin_cg,
fmin_bfgs, fmin_ncg – multivariate local optimizers
fmin_l_bfgs_b, fmin_tnc,
fmin_cobyla – constrained multivariate optimizers

anneal, brute – global optimizers

fminbound, brent, golden, bracket – local scalar minimizers

fsolve – n-dimenstional root-finding

brentq, brenth, ridder, bisect, newton – one-dimensional root-finding

fixed_point – scalar and vector fixed-point finder

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