NAG AD Library
E04 (Opt)
Minimizing or Maximizing a Function

E04 (Opt) Chapter Introduction (FL Interface) – A description of the Chapter and an overview of the algorithms available.

Routine
Mark of
Introduction

Purpose
e04ab_a1w_f 27 nagf_opt_one_var_func_a1w
Minimizes a function of one variable, using function values only
e04dg_a1w_f 26.2 nagf_opt_uncon_conjgrd_comp_a1w
Unconstrained minimum, preconditioned conjugate gradient algorithm, using first derivatives (comprehensive)
e04fc_a1w_f 26.2 nagf_opt_lsq_uncon_mod_func_comp_a1w
Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm, using function values only (comprehensive)
e04gb_a1w_f 26.2 nagf_opt_lsq_uncon_quasi_deriv_comp_a1w (symbolic adjoint mode)
Unconstrained minimum of a sum of squares, combined Gauss–Newton and quasi-Newton algorithm, using first derivatives (comprehensive)
e04us_a1w_f 27 nagf_opt_lsq_gencon_deriv_a1w
Minimum of a sum of squares, nonlinear constraints, dense, active-set SQP method, using function values and optionally first derivatives