1Blurb:: 2Hessians are needed and will be approximated by secant updates (BFGS 3or SR1) from a series of gradient evaluations 4 5Description:: 6The \c quasi_hessians specification means that Hessian information is 7needed and will be approximated using secant updates (sometimes called 8"quasi-Newton updates", though any algorithm that approximates 9Newton's method is a quasi-Newton method). 10 11Compared to finite difference numerical Hessians, secant 12approximations do not expend additional function evaluations in 13estimating all of the second-order information for every point of 14interest. Rather, they accumulate approximate curvature information 15over time using the existing gradient evaluations. 16 17The supported secant approximations include the 18Broyden-Fletcher-Goldfarb-Shanno (BFGS) update (specified with the 19keyword \c bfgs) and the Symmetric Rank 1 (SR1) update (specified with 20the keyword \c sr1). 21 22Topics:: 23Examples:: 24Theory:: 25 26 27 28Faq:: 29See_Also:: responses-no_hessians, responses-numerical_hessians, responses-analytic_hessians, responses-mixed_hessians 30