./math/nlopt, Nonlinear optimization library

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Branch: CURRENT, Version: 2.4.2, Package name: nlopt-2.4.2, Maintainer: pkgsrc-users

NLopt is a free/open-source library for nonlinear optimization,
providing a common interface for a number of different free optimization
outines available online as well as original implementations of various
other algorithms.

Its features include:
- Callable from C, C++, Fortran, Matlab or GNU Octave, Python,
GNU Guile, Julia, GNU R, Lua, and OCaml.
- A common interface for many different algorithms -- try a different
algorithm just by changing one parameter.
- Support for large-scale optimization (some algorithms scalable to
millions of parameters and thousands of constraints).
- Both global and local optimization algorithms.
- Algorithms using function values only (derivative-free) and also
algorithms exploiting user-supplied gradients.
- Algorithms for unconstrained optimization, bound-constrained
optimization, and general nonlinear inequality/equality constraints.


Required to run:
[devel/gmp]

Required to build:
[pkgtools/cwrappers]

Master sites:

SHA1: 838c399d8fffd7aa56b20231e0d7bd3462ca0226
RMD160: 851cdb65ce4de04007df1c038c566d0be8d9917b
Filesize: 2306.633 KB

Version history: (Expand)


CVS history: (Expand)


   2017-03-23 18:07:02 by Joerg Sonnenberger | Files touched by this commit (219)
Log message:
Extend SHA512 checksums to various files I have on my local distfile
mirror.
   2015-11-28 08:33:38 by Wen Heping | Files touched by this commit (5)
Log message:
Import nlopt-2.4.2 as math/nlopt.

NLopt is a free/open-source library for nonlinear optimization,
providing a common interface for a number of different free optimization
outines available online as well as original implementations of various
other algorithms.

Its features include:
- Callable from C, C++, Fortran, Matlab or GNU Octave, Python,
  GNU Guile, Julia, GNU R, Lua, and OCaml.
- A common interface for many different algorithms -- try a different
  algorithm just by changing one parameter.
- Support for large-scale optimization (some algorithms scalable to
  millions of parameters and thousands of constraints).
- Both global and local optimization algorithms.
- Algorithms using function values only (derivative-free) and also
  algorithms exploiting user-supplied gradients.
- Algorithms for unconstrained optimization, bound-constrained
  optimization, and general nonlinear inequality/equality constraints.

Reviewed by:	wiz@