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# 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)

  Minimize the sum of squares of a set of equations.

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

  Parameters : | func : callable should take at least one (possibly length N vector) argument and returns M floating point numbers. x0 : ndarray The starting estimate for the minimization. args : tuple Any extra arguments to func are placed in this tuple. Dfun : callable 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 : bool non-zero to return all optional outputs. col_deriv : bool non-zero to specify that the Jacobian function computes derivatives down the columns (faster, because there is no transpose operation). ftol : float Relative error desired in the sum of squares. xtol : float Relative error desired in the approximate solution. gtol : float Orthogonality desired between the function vector and the columns of the Jacobian. maxfev : int 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 : float 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 : float A parameter determining the initial step bound (`factor * || diag * x||`). Should be in interval `(0.1, 100)`. diag : sequence N positive entries that serve as a scale factors for the variables.
  > Returns : | x : ndarray The solution (or the result of the last iteration for an unsuccessful call). cov_x : ndarray Uses the fjac and ipvt optional outputs to construct an estimate of the jacobian around the solution. `None` if a singular matrix encountered (indicates very flat curvature in some direction). This matrix must be multiplied by the residual standard deviation to get the covariance of the parameter estimates – see curve_fit. infodict : dict a dictionary of optional outputs with the key s:
  >     - '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 : str A string message giving information about the cause of failure. ier : int 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.

  Notes

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

  cov_x is a Jacobian approximation to the Hessian of the least squares objective function. This approximation assumes that the objective function is based on the difference between some observed target data (ydata) and a (non-linear) function of the parameters f(xdata, params)

      func(params) = ydata - f(xdata, params)

  so that the objective function is

        min   sum((ydata - f(xdata, params))**2, axis=0)
      params

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