eigen - C++ library for linear algebra

	Commit message (Collapse)	Author	Age
*	Record whether the underlying tensor storage can be accessed directly during ↵	Benoit Steiner	2016-01-19
\| \| \| \|	the evaluation of an expression.
*	Removed an incorrect assertion.	Benoit Steiner	2015-07-27
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*	Fixed a typo in an assertion.	Benoit Steiner	2015-07-24
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*	Silenced several compilation warnings	Benoit Steiner	2015-02-10
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*	Improved support for RowMajor tensors	Benoit Steiner	2015-01-14
\| \| \| \|	Misc fixes and API cleanups.
*	Misc improvements and cleanups	Benoit Steiner	2014-10-13
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*	Added suppor for in place evaluation to simple tensor expressions.	Benoit Steiner	2014-08-13
\| \| \| \|	Use mempy to speedup tensor copies whenever possible.
*	Reworked the expression evaluation mechanism in order to make it possible to ↵	Benoit Steiner	2014-06-13
\| \| \| \| \| \| \| \|	efficiently compute convolutions and contractions in the future: * The scheduling of computation is moved out the the assignment code and into a new TensorExecutor class * The assignment itself is now a regular node on the expression tree * The expression evaluators start by recursively evaluating all their subexpressions if needed
*	TensorEval are now typed on the device: this will make it possible to use ↵	Benoit Steiner	2014-06-10
\| \| \| \| \| \|	partial template specialization to optimize the strategy of each evaluator for each device type. Started work on partial evaluations.
*	Added support for tensor contractions	Benoit Steiner	2014-06-04
\| \| \| \| \|	Updated expression evaluation mechanism to also compute the size of the tensor result Misc fixes and improvements.
*	Vectorized the evaluation of tensor expression (using SSE, AVX, NEON, ...)	Benoit Steiner	2014-05-16
\| \| \| \| \|	Added the ability to parallelize the evaluation of a tensor expression over multiple cpu cores. Added the ability to offload the evaluation of a tensor expression to a GPU.
*	Extended support for Tensors:	Benoit Steiner	2014-04-28
	* Added ability to map a region of the memory to a tensor * Added basic support for unary and binary coefficient wise expressions, such as addition or square root * Provided an emulation layer to make it possible to compile the code with compilers (such as nvcc) that don't support cxx11.