The std::isnan method can be safely used in host code (if the compiler
is not moron). There is no device code which uses isnan so the
TNL::isNan function is useless.

This reverts commit 0eff2f63.

Refactoring: replaced TNL::SharedPointer with std::shared_ptr for the linear solver and preconditioner

The DeviceType capability does not matter because these objects are
never passed to the device and we need to pass "shared_ptr<Derived>" as
"shared_ptr<Base>".

The vector view types have to be passed by value, because pass by
reference does not consider the converting constructors.

It is actually used only in 2 methods and unnecessarily complicates the
interface for all other solvers.

They are disabled and never really worked for these objects...

It is not needed, since CUSPARSE is included in all versions of CUDA
since at least 7.0.

Also note that CUSPARSE is needed only for some tests and legacy code
(tnl-benchmark-spmv and wrappers in Matrices::CSR) and experimental code
(Preconditioners::ILU0). Everything is still guarded by the
HAVE_CUSPARSE macro, so the user can pass -DHAVE_CUSPARSE -lcusparse to
the compiler to enable the features.

- separate data types for input arrays of binary operations
  (necessary for the equality comparison of two arrays of different
  type)
- added separate template parameter for ResultType, which defaults to
  the RealType
  (the goal is to allow sum of bool vectors into int instead of bool)