## Is Eigen a good library?

Eigen is reliable. Algorithms are carefully selected for reliability. Reliability trade-offs are clearly documented and extremely safe decompositions are available. Eigen is thoroughly tested through its own test suite (over 500 executables), the standard BLAS test suite, and parts of the LAPACK test suite.

**How good is Eigen?**

For operations involving complex expressions, Eigen is inherently faster than any BLAS implementation because it can handle and optimize a whole operation globally — while BLAS forces the programmer to split complex operations into small steps that match the BLAS fixed-function API, which incurs inefficiency due to …

**Is Eigen faster than LAPACK?**

Eigen beats LAPACK using optimization flags ( -O3 ) and a good compiler (GCC, Clang).

### Does Eigen use MKL?

Eigen: Using Intel® MKL from Eigen. Since Eigen version 3.1 and later, users can benefit from built-in Intel® Math Kernel Library (MKL) optimizations with an installed copy of Intel MKL 10.3 (or later). Intel MKL provides highly optimized multi-threaded mathematical routines for x86-compatible architectures.

**What is the best numerical library C C++?**

The Intel MKL libraries (which are the BLAS/LAPACK implementation provided by Intel) remain the best choice if you are mainly interested in speed and you are using Intel processors. Unfortunately they are not C++ and they are quite complex to use and the interface is not so easy.

**Does Eigen use Blas?**

Eigen: Using BLAS/LAPACK from Eigen. Since Eigen version 3.3 and later, any F77 compatible BLAS or LAPACK libraries can be used as backends for dense matrix products and dense matrix decompositions. When doing so, a number of Eigen’s algorithms are silently substituted with calls to BLAS or LAPACK routines.

## Does Eigen use Lapack?

**Is Intel MKL free?**

Intel Math Kernel Library (MKL) Intel MKL is free to use in any commercial and academic purposes. Although it needs no charge, you have to register (free) to get the MKL package.

**What is the difference between Cmath and math H?**

[cmath] defines symbols in the std namespace, and may also define symbols in the global namespace. [math. h] defines symbols in the global namespace, and may also define symbols in the std namespace. if you include the former and use an unqualified symbol, it may compile with one compiler but not with another.

### Which library is used for handling numbers?

IMSL Numerical Libraries are libraries of numerical analysis functionality implemented in standard programming languages like C, Java, C# .

**Is MKL fast?**

Speedup > 1 means MKL is faster. Speedup < 0 means “standard” numpy (using openBLAS) is faster. As you can see, differences are small. For some functions there is small (~1.1x) speedup.

**What libraries are included in the eigen3 benchmarks?**

March 2009: Early version of eigen3, includes Eigen w/o vectorization, MKL, Goto, Atlas, and ACML. Here is the list of the libraries included in the following benchmarks: eigen3: ourselves, with the default options (SSE2 vectorization enabled). eigen2: the previous stable version of Eigen, with the default options (SSE2 vectorization enabled).

## Is Eigen a good template library?

Eigen is multi-platform, and is actually being used on a number of different operating systems, hardware platforms, and compilers. Eigen, compared to certain other C++ template libraries, is relatively easy on the compiler. Compilation times stay reasonable — we are very careful about that.

**How does Eigen compare to MKL for large matrices?**

The benchmark available on this page from the Eigen website tells you than Eigen (with its own BLAS) gives timings similar to the MKL for large matrices (n = 1000).

**What is the Eigen project?**

The Eigen project started when some hackers from the large KDE meta-project realized the need for a single unified matrix library. Some other libraries do satisfy very well certain specialized needs, but none is as versatile as Eigen, has such a nice API, etc.