| E02ADF
| Least squares curve fit, by polynomials, arbitrary data points |
| E02AFF
| Least squares polynomial fit, special data points (including interpolation) |
| E02AGF
| Least squares polynomial fit, values and derivatives may be constrained, arbitrary data points |
| E02BAF
| Least squares curve cubic spline fit (including interpolation) |
| E02CAF
| Least squares surface fit by polynomials, data on lines parallel to one independent coordinate axis |
| E02DAF
| Least squares surface fit, bicubic splines |
| E04ABF
| Minimum, function of one variable using function values only |
| E04BBF
| Minimum, function of one variable, using first derivative |
| E04CBF
| Unconstrained minimization using simplex algorithm, function of several variables using function values only |
| E04DGF
| Unconstrained minimum, preconditioned conjugate gradient algorithm, function of several variables using first derivatives (comprehensive) |
| 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) |
| E04JCF
| Minimum by quadratic approximation, function of several variables, simple bounds, using function values only |
| 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) |
| E04MXF
| Reads MPS data file defining LP, QP, MILP or MIQP problem |
| E04NCF
| Convex QP problem or linearly-constrained linear least squares problem (dense) |
| E04NFF
| QP problem (dense) |
| E04NKF
| LP or QP problem (sparse) |
| E04NQF
| LP or QP problem (suitable for sparse problems) |
| E04PCF
| 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 |
| E04UCF
| Minimum, function of several variables, sequential QP method, nonlinear constraints, using function values and optionally first derivatives (comprehensive) |
| 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) |
| E04USF
| Minimum of a sum of squares, nonlinear constraints, sequential QP method, using function values and optionally first derivatives (comprehensive) |
| E04VHF
| General sparse nonlinear optimizer |
| E04VJF
| Determine the pattern of nonzeros in the Jacobian matrix for E04VHF |
| E04WDF
| Solves the nonlinear programming (NP) problem |
| 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) |