a00ad | a00ad: Library identification, details of implementation, major and minor marks |

e04ab | e04ab: Minimum, function of one variable using function values only |

e04bb | e04bb: Minimum, function of one variable, using first derivative |

e04cb | e04cb: Unconstrained minimization using simplex algorithm, function of several variables using function values only |

e04dg | e04dg: Unconstrained minimum, preconditioned conjugate gradient algorithm, function of several variables using first derivatives (comprehensive) |

e04fc | e04fc: Unconstrained minimum of a sum of squares, combined Gauss-Newton and modified Newton algorithm using function values only (comprehensive) |

e04fy | e04fy: Unconstrained minimum of a sum of squares, combined Gauss-Newton and modified Newton algorithm using function values only (easy-to-use) |

e04gd | e04gd: Unconstrained minimum of a sum of squares, combined Gauss-Newton and modified Newton algorithm using first derivatives (comprehensive) |

e04gy | e04gy: Unconstrained minimum of a sum of squares, combined Gauss-Newton and quasi-Newton algorithm, using first derivatives (easy-to-use) |

e04gz | e04gz: Unconstrained minimum of a sum of squares, combined Gauss-Newton and modified Newton algorithm using first derivatives (easy-to-use) |

e04hc | e04hc: Check user's function for calculating first derivatives of function |

e04hd | e04hd: Check user's function for calculating second derivatives of function |

e04he | e04he: Unconstrained minimum of a sum of squares, combined Gauss-Newton and modified Newton algorithm, using second derivatives (comprehensive) |

e04hy | e04hy: Unconstrained minimum of a sum of squares, combined Gauss-Newton and modified Newton algorithm, using second derivatives (easy-to-use) |

e04jc | e04jc: Minimum by quadratic approximation, function of several variables, simple bounds, using function values only |

e04jy | e04jy: Minimum, function of several variables, quasi-Newton algorithm, simple bounds, using function values only (easy-to-use) |

e04kd | e04kd: Minimum, function of several variables, modified Newton algorithm, simple bounds, using first derivatives (comprehensive) |

e04ky | e04ky: Minimum, function of several variables, quasi-Newton algorithm, simple bounds, using first derivatives (easy-to-use) |

e04kz | e04kz: Minimum, function of several variables, modified Newton algorithm, simple bounds, using first derivatives (easy-to-use) |

e04lb | e04lb: Minimum, function of several variables, modified Newton algorithm, simple bounds, using first and second derivatives (comprehensive) |

e04ly | e04ly: Minimum, function of several variables, modified Newton algorithm, simple bounds, using first and second derivatives (easy-to-use) |

e04mf | e04mf: LP problem (dense) |

e04nc | e04nc: Convex QP problem or linearly-constrained linear least squares problem (dense) |

e04nf | e04nf: QP problem (dense) |

e04nk | e04nk: LP or QP problem (sparse) |

e04nq | e04nq: LP or QP problem (suitable for sparse problems) |

e04uc | e04uc: Minimum, function of several variables, sequential QP method, nonlinear constraints, using function values and optionally first derivatives (comprehensive) |

e04uf | e04uf: Minimum, function of several variables, sequential QP method, nonlinear constraints, using function values and optionally first derivatives (reverse communication, comprehensive) |

e04ug | e04ug: NLP problem (sparse) |

e04us | e04us: Minimum of a sum of squares, nonlinear constraints, sequential QP method, using function values and optionally first derivatives (comprehensive) |

e04vj | e04vj: Determine the pattern of nonzeros in the Jacobian matrix for e04vh |

e04wd | e04wd: Solves the nonlinear programming (NP) problem |

e04xa | e04xa: Estimate (using numerical differentiation) gradient and/or Hessian of a function |

e04ya | e04ya: Check user's function for calculating Jacobian of first derivatives |

e04yb | e04yb: Check user's function for calculating Hessian of a sum of squares |

e04yc | e04yc: Covariance matrix for nonlinear least squares problem (unconstrained) |

e05jb | e05jb: Global optimization by multi-level coordinate search, simple bounds, using function values only |

f08fa | f08fa: Computes all eigenvalues and, optionally, eigenvectors of a real symmetric matrix |

g02aa | g02aa: Computes the nearest correlation matrix to a real square matrix, using the method of Qi and Sun |

g02ab | g02ab: Computes the nearest correlation matrix to a real square matrix, augmented g02aa to incorporate weights and bounds |

g02ae | g02ae: Computes the nearest correlation matrix with k-factor structure to a real square matrix |

NAGFWrappers | Provides interfaces to NAG Fortran Library |

s17dc | s17dc: Bessel functions Y_nu + a(z), real a >= 0, complex z, nu = 0 , 1 , 2 , . . . |

s17de | s17de: Bessel functions J_nu + a(z), real a >= 0, complex z, nu = 0 , 1 , 2 , . . . |

s17dg | s17dg: Airy functions Ai(z) and Ai'(z), complex z |

s17dh | s17dh: Airy functions Bi(z) and Bi'(z), complex z |

s17dl | s17dl: Hankel functions H_nu + a^(j)(z), j = 1 , 2, real a >= 0, complex z, nu=0 , 1 , 2 , . . . |

s18dc | s18dc: Modified Bessel functions K_nu + a(z), real a >= 0, complex z, nu = 0 , 1 , 2 , . . . |

s18de | s18de: Modified Bessel functions I_nu + a(z), real a >= 0, complex z, nu = 0 , 1 , 2 , . . . |

s18gk | s18gk: Bessel function of the 1st kind J_alpha +/- n(z) |

s22aa | s22aa: Legendre functions of 1st kind P_n^m(x) or overlineP_n^m(x) |

x02aj | x02aj: The machine precision |

x02al | x02al: The largest positive model number |