Dear NLopt users,
NLopt version 2.0 is now available from the NLopt web page (http://ab-initio.mit.edu/nlopt
). The major change is a new object-style C API that is much more
extensible than the old API (which continues to be supported for
backwards compatibility).
We also include a C++ style API (nlopt.hpp), a Python interface, and a
GNU Guile (Scheme) interface.
Nonlinear inequality constraints are now also supported, and there are
numerous other changes noted below.
The documentation of the new API on the web page is currently
incomplete, but will appear over the next few days. The tutorial has
been updated for the new API, however.
Regards,
Steven G. Johnson
NLopt 2.0 (15 June 2010)
* New C API, that works by creating an nlopt_opt "object" and then
calling
functions to set the optimization parameters -- much more extensible
than the old API (which is preserved for backwards compatibility).
(Updated Fortran, Matlab, and GNU Octave wrappers as well.)
* C++ nlopt.hpp wrapper around C API, allowing namespaces, object
constructors/destructors, std::vector<double>, and exceptions
to be exploited.
* New nlopt wrappers callable from Python and GNU Guile, generated
with the help of SWIG.
* New 'man nlopt' manual page documenting new API.
* New AUGLAG algorithm(s) implementing an augmented-Lagrangian method
proposed by Birgin and Martinez (2008), which supports nonlinear
equality and inequality constraints "wrapped around" other
local/global optimization methods.
* Added API for nonlinear equality constraints (currently only
supported by AUGLAG and ISRES algorithms).
* Support inequality constraints directly in ORIG_DIRECT algorithms
(no need to return NaN when constraint is violated).
* Inequality/equality constraints now have optional tolerances that
are used as conditions in stopping criteria.
* Pseudo-randomize simplex steps in COBYLA algorithm, improving
robustness
by avoiding accidentally taking steps that don't improve conditioning
(which seems to happen sometimes with active bound constraints). The
algorithm remains deterministic (a deterministic seed is used),
however.
* Allow COBYLA to increase the trust-region radius if the predicted
improvement
was approximately right and the simplex is OK, following a suggestion
in the SAS manual for PROC NLP that seems to improve convergence
speed.
* Added nlopt_force_stop function to force a (graceful) halt to
the optimization, and corresponding NLOPT_FORCED_STOP return code.
* Improved thread-safety in random-number generation: thread-local
storage is used for random-number state, on compilers that support
it (e.g. gcc, Intel, Microsoft), to make the generation thread-safe.
In this case, the random-number seed must be set per-thread.
* Return an error in global-search algorithms if the domain is not
finite.
* Use stdcall convention on Windows; thanks to Alan Young for the
suggestion.
* Added missing absolute-tolerance criteria in Luksan algorithms; thanks
to Greg Nicholas for the bug report.
* Fixed compilation under C++, and use C++ compiler for everything in
--with-cxx mode; thanks to Greg Nicholas for the bug report.
* In MMA, only stop at minf_max/stopval if the point is feasible.
* Fix Matlab mex file to not include unnecessary nlopt-util.h file,
simplifying Windows compilation.
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