cling

History

Vassil Vassilev 6130148a58 Bump clad version to v0.5.

The new release includes some improvements in both Forward and
Reverse mode:
* Extend the way to specify a dependent variables. Consider function,
  `double f(double x, double y, double z) {...}`, `clad::differentiate(f, "z")`
  is equivalent to `clad::differentiate(f, 2)`. `clad::gradient(f, "x, y")`
  differentiates with respect to `x` and `y` but not `z`. The gradient results
  are stored in a `_result` parameter in the same order as `x` and `y` were
  specified. Namely, the result of `x` is stored in `_result[0]` and the result
  of `y` in `_result[1]`. If we invert the arguments specified in the string to
  `clad::gradient(f, "y, x")` the results will be stored inversely.
* Enable recursive differentiation.
* Support single- and multi-dimensional arrays -- works for arrays with constant
  size like `double A[] = {1, 2, 3};`, `double A[3];` or `double A[1][2][3][4];`

See more at: https://github.com/vgvassilev/clad/blob/v0.5/docs/ReleaseNotes.md

2019-04-08 20:59:15 +02:00

demo

Several fixes for Windows

2018-03-22 13:44:16 +01:00

driver

Reduce the libraries we link to.

2018-11-21 17:15:14 +01:00

Jupyter

Use CLING_OPTS env variable to pass extra arguments to jupyter-cling-kernel

2018-03-23 16:59:08 +01:00

libcling

Add missing link libraries.