| Chapter Introduction | |
| E04ABF | Minimum, function of one variable using function values only |
| E04BBF | Minimum, function of one variable, using first derivative |
| E04CCF | Unconstrained minimum, simplex algorithm, function of several variables using function values only (comprehensive) |
| E04DGF | Unconstrained minimum, preconditioned conjugate gradient algorithm, function of several variables using first derivatives (comprehensive) |
| E04DJF | Read optional parameter values for E04DGF from external file |
| E04DKF | Supply optional parameter values to E04DGF |
| E04FCF | Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm using function values only (comprehensive) |
| E04FYF | Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm using function values only (easy-to-use) |
| E04GBF | Unconstrained minimum of a sum of squares, combined Gauss–Newton and quasi-Newton algorithm using first derivatives (comprehensive) |
| E04GDF | Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm using first derivatives (comprehensive) |
| E04GYF | Unconstrained minimum of a sum of squares, combined Gauss–Newton and quasi-Newton algorithm, using first derivatives (easy-to-use) |
| E04GZF | Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm using first derivatives (easy-to-use) |
| E04HCF | Check user's routine for calculating first derivatives of function |
| E04HDF | Check user's routine for calculating second derivatives of function |
| E04HEF | Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm, using second derivatives (comprehensive) |
| E04HYF | Unconstrained minimum of a sum of squares, combined Gauss–Newton and modified Newton algorithm, using second derivatives (easy-to-use) |
| E04JYF | Minimum, function of several variables, quasi-Newton algorithm, simple bounds, using function values only (easy-to-use) |
| E04KDF | Minimum, function of several variables, modified Newton algorithm, simple bounds, using first derivatives (comprehensive) |
| E04KYF | Minimum, function of several variables, quasi-Newton algorithm, simple bounds, using first derivatives (easy-to-use) |
| E04KZF | Minimum, function of several variables, modified Newton algorithm, simple bounds, using first derivatives (easy-to-use) |
| E04LBF | Minimum, function of several variables, modified Newton algorithm, simple bounds, using first and second derivatives (comprehensive) |
| E04LYF | Minimum, function of several variables, modified Newton algorithm, simple bounds, using first and second derivatives (easy-to-use) |
| E04MFF | LP problem (dense) |
| E04MGF | Read optional parameter values for E04MFF from external file |
| E04MHF | Supply optional parameter values to E04MFF |
| E04MZF | Converts MPSX data file defining LP or QP problem to format required by E04NKF |
| E04NCF | Convex QP problem or linearly-constrained linear least-squares problem (dense) |
| E04NDF | Read optional parameter values for E04NCF from external file |
| E04NEF | Supply optional parameter values to E04NCF |
| E04NFF | QP problem (dense) |
| E04NGF | Read optional parameter values for E04NFF from external file |
| E04NHF | Supply optional parameter values to E04NFF |
| E04NKF | LP or QP problem (sparse) |
| E04NLF | Read optional parameter values for E04NKF from external file |
| E04NMF | Supply optional parameter values to E04NKF |
| E04UCF | Minimum, function of several variables, sequential QP method, nonlinear constraints, using function values and optionally first derivatives (forward communication, comprehensive) |
| E04UDF | Read optional parameter values for E04UCF or E04UFF from external file |
| E04UEF | Supply optional parameter values to E04UCF or E04UFF |
| E04UFF | Minimum, function of several variables, sequential QP method, nonlinear constraints, using function values and optionally first derivatives (reverse communication, comprehensive) |
| E04UGF | NLP problem (sparse) |
| E04UHF | Read optional parameter values for E04UGF from external file |
| E04UJF | Supply optional parameter values to E04UGF |
| E04UNF * | Minimum of a sum of squares, nonlinear constraints, sequential QP method, using function values and optionally first derivatives (comprehensive) |
| E04UQF | Read optional parameter values for E04UNF from external file |
| E04URF | Supply optional parameter values to E04UNF |
| E04USF | Minimum of a sum of squares, nonlinear constraints, sequential QP method, using function values and optionally first derivatives (comprehensive) |
| E04WBF | Initialization routine for E04DGA, E04MFA, E04NCA, E04NFA, E04NKA, E04UCA, E04UFA, E04UGA and E04USA |
| E04XAF | Estimate (using numerical differentiation) gradient and/or Hessian of a function |
| E04YAF | Check user's routine for calculating Jacobian of first derivatives |
| E04YBF | Check user's routine for calculating Hessian of a sum of squares |
| E04YCF | Covariance matrix for nonlinear least-squares problem (unconstrained) |
| E04ZCF | Check user's routines for calculating first derivatives of function and constraints |