update minimize to use MLELoss class

			# peak_loss = MemoizeJacHess(peak_loss)

			peak_loss = MLELoss()

			result = minimize(peak_loss,

				jac  = peak_loss.derivative, #grad,

				hess = peak_loss.hessian, #hess,

				jac  = peak_loss.gradient, #gaussian.gradient, #peak_loss.derivative,

				hess = peak_loss.hessian, #peak_loss.hessian,

				x0=params_init, bounds=tuple(zip(params_lbnd,params_ubnd)),

				# method='L-BFGS-B', options={'maxiter':1000, 'maxfun':1000, 'maxls':20, 'maxcor':100, 'ftol':1.e-5, 'gtol':1.e-5, 'disp':False}

				# method='BFGS', options={'maxiter':1000, 'gtol':1.e-5, 'norm':np.inf, 'disp':True}

				# method='CG', options={'maxiter':1000, 'gtol':1.e-5, 'norm':np.inf, 'disp':True}

				method='Newton-CG', options={'maxiter':1000, 'xtol':1.e-3, 'disp':True}

				method='Newton-CG', options={'maxiter':1000, 'xtol':1.e-5, 'disp':True}

				# method='Nelder-Mead', options={'maxiter':1000, 'disp':False}

				# method='TNC', options={'scale':None, 'maxfun':1000, 'ftol':1.e-3, 'gtol':1.e-5, 'disp':True}

				# method='dogleg', options={'maxiter':1000, 'tol':1.e-6, 'gtol':1.e-8, 'disp':True}

				# method='trust-krylov', options={'maxiter':1000, 'tol':1.e-6, 'inexact':False, 'disp':True}

				# method='trust-exact', options={'maxiter':1000, 'gtol':1.e-4, 'disp':True}

				# method='trust-constr', options={'maxiter':1000, 'gtol':1.e-4, 'disp':True}

				)

			# g,dg = gaussian_mixture(result.x[nbkgr:],fit_points,npeaks=1,covariance_parameterization=covariance_parameterization,return_gradient=True)