@Article{Luksan:2009:ALA,
  author =       "Ladislav Luk\v{s}an and Ctirad Matonoha and Jan Vl\v{c}ek",
  title =        "Algorithm 896: {LSA}: Algorithms for Large-Scale Optimization",
  journal =      "{ACM} Transactions on Mathematical Software",
  volume =       "36",
  number =       "3",
  pages =        "16:1--16:29",
  URL =          "http://doi.acm.org/10.1145/1527286.1527290"
  month  =       jul,
  year =         2009,
  accepted =     "January 14 2009",
  abstract =     "
                 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 method
                 and the inverse column-update quasi-Newton method,
                 respectively. Besides the description of methods and
                 codes, we propose computational experiments which
                 demonstrate the efficiency of the proposed algorithms.",
}