E04 – minimizing or maximizing a function
- E04 Introduction
- e04ab – Minimum, function of one variable using function values only
- nag_opt_one_var_func
- e04bb – Minimum, function of one variable, using first derivative
- nag_opt_one_var_deriv
- e04cb – Unconstrained minimization using simplex algorithm, function of several variables using function values only
- nag_opt_uncon_simplex
- e04dg – Unconstrained minimum, preconditioned conjugate gradient algorithm, function of several variables using first derivatives
(comprehensive)
- nag_opt_uncon_conjgrd_comp
- e04dk – Supply optional parameter values to e04dg
- nag_opt_uncon_conjgrd_option_string
- e04fc – Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm using function values only
(comprehensive)
- nag_opt_lsq_uncon_mod_func_comp
- e04fy – Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm using function values only
(easy-to-use)
- nag_opt_lsq_uncon_mod_func_easy
- e04gb – Unconstrained minimum of a sum of squares, combined Gauss–Newton and quasi-Newton algorithm using first derivatives (comprehensive)
- nag_opt_lsq_uncon_quasi_deriv_comp
- e04gd – Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm using first derivatives (comprehensive)
- nag_opt_lsq_uncon_mod_deriv_comp
- e04gy – Unconstrained minimum of a sum of squares, combined Gauss–Newton and quasi-Newton algorithm, using first derivatives (easy-to-use)
- nag_opt_lsq_uncon_quasi_deriv_easy
- e04gz – Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm using first derivatives (easy-to-use)
- nag_opt_lsq_uncon_mod_deriv_easy
- e04hc – Check user's function for calculating first derivatives of function
- nag_opt_check_deriv
- e04hd – Check user's function for calculating second derivatives of function
- nag_opt_check_deriv2
- e04he – Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm, using second derivatives (comprehensive)
- nag_opt_lsq_uncon_mod_deriv2_comp
- e04hy – Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm, using second derivatives (easy-to-use)
- nag_opt_lsq_uncon_mod_deriv2_easy
- e04jc – Minimum by quadratic approximation, function of several variables, simple bounds, using function values only
- nag_opt_bounds_bobyqa_func
- e04jy – Minimum, function of several variables, quasi-Newton algorithm, simple bounds, using function values only (easy-to-use)
- nag_opt_bounds_quasi_func_easy
- e04kd – Minimum, function of several variables, modified Newton algorithm, simple bounds, using first derivatives (comprehensive)
- nag_opt_bounds_mod_deriv_comp
- e04ky – Minimum, function of several variables, quasi-Newton algorithm, simple bounds, using first derivatives (easy-to-use)
- nag_opt_bounds_quasi_deriv_easy
- e04kz – Minimum, function of several variables, modified Newton algorithm, simple bounds, using first derivatives (easy-to-use)
- nag_opt_bounds_mod_deriv_easy
- e04lb – Minimum, function of several variables, modified Newton algorithm, simple bounds, using first and second derivatives (comprehensive)
- nag_opt_bounds_mod_deriv2_comp
- e04ly – Minimum, function of several variables, modified Newton algorithm, simple bounds, using first and second derivatives (easy-to-use)
- nag_opt_bounds_mod_deriv2_easy
- e04mf – LP problem (dense)
- nag_opt_lp_solve
- e04mh – Supply optional parameter values to e04mf
- nag_opt_lp_option_string
- e04mx – Reads MPS data file defining LP, QP, MILP or MIQP problem
- nag_opt_miqp_mps_read
- e04nc – Convex QP problem or linearly-constrained linear least squares problem (dense)
- nag_opt_lsq_lincon_solve
- e04ne – Supply optional parameter values to e04nc
- nag_opt_lsq_lincon_option_string
- e04nf – QP problem (dense)
- nag_opt_qp_dense_solve
- e04nh – Supply optional parameter values to e04nf
- nag_opt_qp_dense_option_string
- e04nk – LP or QP problem (sparse)
- nag_opt_qpconvex1_sparse_solve
- e04nm – Supply optional parameter values to e04nk
- nag_opt_qpconvex1_sparse_option_string
- e04np – Initialization function for e04nq
- nag_opt_qpconvex2_sparse_init
- e04nq – LP or QP problem (suitable for sparse problems)
- nag_opt_qpconvex2_sparse_solve
- e04ns – Set a single option for e04nq from a character string
- nag_opt_qpconvex2_sparse_option_string
- e04nt – Set a single option for e04nq from an integer argument
- nag_opt_qpconvex2_sparse_option_integer_set
- e04nu – Set a single option for e04nq from a real argument
- nag_opt_qpconvex2_sparse_option_double_set
- e04nx – Get the setting of an integer valued option of e04nq
- nag_opt_qpconvex2_sparse_option_integer_get
- e04ny – Get the setting of a real valued option of e04nq
- nag_opt_qpconvex2_sparse_option_double_get
- e04pc – Computes the least squares solution to a set of linear equations subject to fixed upper and lower bounds on the variables.
An option is provided to return a minimal length solution if a solution is not unique
- nag_bnd_lin_lsq
- e04uc – Minimum, function of several variables, sequential QP method, nonlinear constraints, using function values and optionally first derivatives (comprehensive)
- nag_opt_nlp1_solve
- e04ue – Supply optional parameter values to e04uc or e04uf
- nag_opt_nlp1_option_string
- e04uf – Minimum, function of several variables, sequential QP method, nonlinear constraints, using function values and optionally first derivatives (reverse communication, comprehensive)
- nag_opt_nlp1_rcomm
- e04ug – NLP problem (sparse)
- nag_opt_nlp1_sparse_solve
- e04uj – Supply optional parameter values to e04ug
- nag_opt_nlp1_sparse_option_string
- e04ur – Supply optional parameter values to e04us
- nag_opt_lsq_gencon_deriv_option_string
- e04us – Minimum of a sum of squares, nonlinear constraints, sequential QP method, using function values and optionally first derivatives (comprehensive)
- nag_opt_lsq_gencon_deriv
- e04vg – Initialization function for e04vh
- nag_opt_nlp2_sparse_init
- e04vh – General sparse nonlinear optimizer
- nag_opt_nlp2_sparse_solve
- e04vj – Determine the pattern of nonzeros in the Jacobian matrix for e04vh
- nag_opt_nlp2_sparse_jacobian
- e04vl – Set a single option for e04vh from a character string
- nag_opt_nlp2_sparse_option_string
- e04vm – Set a single option for e04vh from an integer argument
- nag_opt_nlp2_sparse_option_integer_set
- e04vn – Set a single option for e04vh from a real argument
- nag_opt_nlp2_sparse_option_double_set
- e04vr – Get the setting of an integer valued option of e04vh
- nag_opt_nlp2_sparse_option_integer_get
- e04vs – Get the setting of a real valued option of e04vh
- nag_opt_nlp2_sparse_option_double_get
- e04wb – Initialization function for e04dg, e04mf, e04nc, e04nf, e04uf, e04ug, e04us
- nag_opt_init
- e04wc – Initialization function for e04wd
- nag_opt_nlp2_init
- e04wd – Solves the nonlinear programming (NLP) problem
- nag_opt_nlp2_solve
- e04wf – Set a single option for e04wd from a character string
- nag_opt_nlp2_option_string
- e04wg – Set a single option for e04wd from an integer argument
- nag_opt_nlp2_option_integer_set
- e04wh – Set a single option for e04wd from a real argument
- nag_opt_nlp2_option_double_set
- e04wk – Get the setting of an integer valued option of e04wd
- nag_opt_nlp2_option_integer_get
- e04wl – Get the setting of a real valued option of e04wd
- nag_opt_nlp2_option_double_get
- e04xa – Estimate (using numerical differentiation) gradient and/or Hessian of a function
- nag_opt_estimate_deriv
- e04ya – Check user's function for calculating Jacobian of first derivatives
- nag_opt_lsq_check_deriv
- e04yb – Check user's function for calculating Hessian of a sum of squares
- nag_opt_lsq_check_hessian
- e04yc – Covariance matrix for nonlinear least squares problem (unconstrained)
- nag_opt_lsq_uncon_covariance
