octave-optim 1.3.0-1 / usr / share / octave / packages / optim-1.3.0 / packinfo / NEWS

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optim 1.3.0:
------------

 ** LinearRegression: Two demo codes added ad the confidence band 
                      for the dependent variable can be estimated.

 ** Bugfix in jacobs for functions of more than 2 variables.

 ** Parallel computation of gradients supported by nonlin_residmin,
    nonlin_curvefit, and nonlin_min for default and for complex step
    gradient methods. Introduces dependency on parallel package.

 ** In nonlin_residmin, nonlin_curvefit, nonlin_min, residmin_stat,
    and curvefit_stat, the following names of optimset options had
    to be changed to be consistent with interpretation of abbreviated
    option names:

      dfdp_pstruct            -> df_pstruct,
      inequc_f_idx            -> f_inequc_idx,
      inequc_df_idx           -> df_inequc_idxn,
      equc_f_idx              -> f_equc_idx,
      equc_df_idx             -> df_equc_idx,
      objf_grad_pstruct       -> grad_objf_pstruct,
      objf_hessian_pstruct    -> hessian_objf_pstruct,
      objf                    -> objf_type,
      complex_step_derivative -> complex_step_derivative_f.

 ** The function fminsearch was removed from the optim package, since
    it is available in Octave 3.8.0.

 ** The depricated functions optimset_compat and fminunc_compat are
    now removed.

optim 1.2.2:
------------

 ** Bugfixes in nonlin_min and nonlin_residmin/nonlin_curvefit in
    termination criteria, argument checking, and documentaion.

 ** Bugfix in private/__lm_feasible__.m: Wrong parantheses in checking
    of equality constraints for initial parameters (reported by Marcus
    Schmidt).

optim 1.2.1:
------------

 ** Bugfix in leasqr.m: errors when a function 'options' is in
    namespace.

Summary of important user-visible changes for optim 1.2.0:
-------------------------------------------------------------------

 ** Together with the new backend "lm_feasible" there is now a
    complete suite of backends for optimization with linear and
    general equality and inequality constraints, for scalar valued
    objective functions and for array valued model function, which
    features, a.o., honouring of constraints throughout optimization
    and handling of structure-based parameters. The respective user
    functions (frontends) are

      nonlin_min nonlin_residmin nonlin_curvefit

    together with a user function for statistics

      residmin_stat

 ** The requirement of nonlin_min, nonlin_residmin, and
    nonlin_curvefit for the general constraint functions being able to
    honour an index of constraints has been removed, the respective
    feature is still available by setting some options.

 ** Makefile fixed to work with non-standard linker options e.g on
    Apple.


Summary of important user-visible changes for optim 1.1.0:
-------------------------------------------------------------------

 ** The following functions are new optim 1.1.0:

      powell cauchy nonlin_min

 ** The following functions have been deprecated since they have been
    implemented in Octave core:

      fminunc_compat  optimset_compat

 ** The function `fmin' has been deprecated in favour of using `fminbnd'
    directly. If one really wishes to use the short version, one can
    easily create an alias on an octaverc file (see `doc startup') with
    the following code

      function out=fmin(varargin) out=fminbnd(varargin{:}); endfunction

 ** The package Makefile has been adapted for compatibility with Octave 3.6.0.

 ** Bugfixes on the functions:

      deriv   linprog

 ** The function `line_min' has a configurable setpesize and max evals.

 ** Added possibility to restrict a parameter to samin.

 ** Package is no longer automatically loaded.


Some important changes of the last versions of optim before 1.1.0:
------------------------------------------------------------------

 ** New functions:

      jacobs: complex step derivative approximation

      nonlin_residmin, nonlin_curvefit: Frontends with a general
      interface for constrained residual-based optimization. They
      allow a.o. optimization of structure-based named parameters or
      parameter-arrays. A backend is included, which is derived from
      the algorithm of leasqr, but enables feasible-path optimization
      with linear and general constraints.

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