Guilhem Saurel authored Mar 20, 2018 and Anthony Mallet committed Apr 05, 2018

Changes since 3.0:

* ColMajor/RowMajor support has been improved (cf. %89). Default is back to
  ColMajor since this is Eigen's default mode, but that can be changed with a
  CMake variable.
* Allocation checking has been improved (cf. %92).
* Multiplots are now available with the matplotlib plotting backend (cf. %94).
* Added vector_t and bool to the solver parameter types. As a consequence,
  std::string parameters should not rely on automatic conversion from const
  char* (cf. 7a0bbb7).
* Renamed scale[s]* to scaling* (cf. 434559c). Previous methods/typedefs are
  currently kept for backward compatibility, but marked as deprecated.
* Add new StructuredInput helper (cf. %96).
* Add support for matrix plotting with matplotlib (cf. 5abd27e / af48e9b).

Changes since 2.0:

* New features:
    * Lots of new functions (cos, sin, etc.), operators (plus, minus, scalar,
      map, etc.) and decorators (cached function etc.),
    * Callback system (logger, callback multiplexer),
    * Simple Matplotlib support for visualization,
    * Function pools,
    * Set argument names (useful for plotting).
* Improvements:
    * Support Eigen::Ref: now RobOptim functions accept blocks/segments of
      Eigen matrices as input,
    * Improved CachedFunction with LRU cache,
    * Automatic conversion of constraints/cost function types when creating
      problems,
    * Faster forward-difference rule for finite differences.
* Other changes:
    * Removed exception specifiers (void function(...) throw())
    * Storage order was changed from Eigen's default (column-major) to
      row-major. The storage order is available in the GenericFunctionTraits
      (StorageOrder).
    * Move metaprogramming magic to roboptim/core/detail/utility.hh
    * Merge roboptim/core/visualization/util.hh with roboptim/core/util.hh

Changes since 1.0:

The version 2.0 of roboptim-core now depends on Eigen for matrix computations
by default.
Traits allow the user to use its own type. Support for Eigen dense and sparse
matrices is built-in.