Chapter Introduction
NAG C Library Manual

## e04 – Minimizing or Maximizing a Function

 RoutineName Mark ofIntroduction Purpose e04abc Example Text 5 nag_opt_one_var_no_deriv Minimizes a function of one variable, using function values only e04bbc Example Text 5 nag_opt_one_var_deriv Minimizes a function of one variable, requires first derivatives e04ccc Example Text Example Data 4 nag_opt_simplex Unconstrained minimization using simplex algorithm e04dgc Example Text Example Data 2 nag_opt_conj_grad Unconstrained minimization using conjugate gradients e04fcc Example Text Example Data 2 nag_opt_lsq_no_deriv Unconstrained nonlinear least squares (no derivatives required) e04gbc Example Text Example Data 2 nag_opt_lsq_deriv Unconstrained nonlinear least squares (first derivatives required) e04hcc Example Text 2 nag_opt_check_deriv Derivative checker for use with e04kbc e04hdc Example Text 5 nag_opt_check_2nd_deriv Checks second derivatives of a user-defined function e04jbc Example Text Example Data 2 nag_opt_bounds_no_deriv Bound constrained nonlinear minimization (no derivatives required) e04kbc Example Text Example Data 2 nag_opt_bounds_deriv Bound constrained nonlinear minimization (first derivatives required) e04lbc Example Text Example Data 5 nag_opt_bounds_2nd_deriv Solves bound constrained problems (first and second derivatives required) e04mfc Example Text Example Data 2 nag_opt_lp Linear programming e04myc 5 nag_opt_sparse_mps_free Free memory allocated by e04mzc e04mzc Example Text Example Data 5 nag_opt_sparse_mps_read Read MPSX data for sparse LP or QP problem from a file e04ncc Example Text Example Data 5 nag_opt_lin_lsq Solves linear least-squares and convex quadratic programming problems (non-sparse) e04nfc Example Text Example Data 2 nag_opt_qp Quadratic programming e04nkc Example Text Example Data 5 nag_opt_sparse_convex_qp Solves sparse linear programming or convex quadratic programming problems e04ucc Example Text Example Data 4 nag_opt_nlp Minimization with nonlinear constraints using a sequential QP method e04ugc Example Text Example Data 6 nag_opt_nlp_sparse NLP problem (sparse) e04unc Example Text Example Data 5 nag_opt_nlin_lsq Solves nonlinear least-squares problems using the sequential QP method e04xac Example Text 5 nag_opt_estimate_deriv Computes an approximation to the gradient vector and/or the Hessian matrix for use with e04ucc and other nonlinear optimization functions e04xxc 2 nag_opt_init Initialisation function for option setting e04xyc 2 nag_opt_read Read options from a text file e04xzc 2 nag_opt_free Memory freeing function for use with option setting e04yac Example Text Example Data 2 nag_opt_lsq_check_deriv Least-squares derivative checker for use with e04gbc e04ycc Example Text Example Data 2 nag_opt_lsq_covariance Covariance matrix for nonlinear least-squares

Chapter Introduction
NAG C Library Manual

© The Numerical Algorithms Group Ltd, Oxford UK. 2002