We present fourteen basic FORTRAN subroutines for large-scale unconstrained and box constrained optimization and large-scale systems of nonlinear equations. Subroutines {\tt PLIS} and {\tt PLIP}, intended for dense general optimization problems, are based on limited-memory variable metric methods. Subroutine {\tt PNET}, also intended for dense general optimization problems, is based on an inexact truncated Newton method. Subroutines {\tt PNED} and {\tt PNEC}, intended for sparse general optimization problems, are based on modifications of the discrete Newton method. Subroutines {\tt PSED} and {\tt PSEC}, intended for partially separable optimization problems, are based on partitioned variable metric updates. Subroutine {\tt PSEN}, intended for nonsmooth partially separable optimization problems, is based on partitioned variable metric updates and on an aggregation of subgradients. Subroutines {\tt PGAD} and {\tt PGAC}, intended for sparse nonlinear least squares problems, are based on modifications and corrections of the Gauss-Newton method. Subroutine {\tt PMAX}, intended for minimization of a maximum value (minimax), is based on the primal line-search interior-point method. Subroutine {\tt PSUM}, intended for minimization of a sum of absolute values, is based on the primal trust-region interior-point method. Subroutines {\tt PEQN} and {\tt PEQL}, intended for sparse systems of nonlinear equations, are based on the discrete Newton and the inverse column-update quasi-Newton methods, respectively. Besides the description of methods and codes, we propose computational experiments which demonstrate the efficiency of the proposed algorithms.
Citation
Technical Report No. V999, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague 2007.