Eigen

by Benoît Jacob & Gaël Guennebaud

Eigen is a C++ template library for linear algebra, matrix and vector operations, numerical solvers, and related algorithms.

Operating system: Windows

Publisher: Benoît Jacob & Gaël Guennebaud

Antivirus check: passed

Last revision: Last week

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Eigen is a high-performance C++ numerical library for linear algebra. It supports a wide range of operations, including matrix and vector operations, linear system solves, eigenvalues and eigenvectors, and decompositions such as QR and SVD. Eigen also provides error-free numerical derivatives and Jacobians.

Eigen allows for efficient and reliable computation of complex mathematical operations through its optimized linear algebra library.
Features:

Matrix and Vector operations:
Eigen provides efficient operations for vectors and matrices such as multiplication, addition, subtraction, and scalar multiplication. It also supports element-wise operations such as element-wise multiplication and division.

Linear System Solves:
Eigen provides efficient algorithms for solving linear systems of equations. It supports direct methods such as LU and Cholesky decomposition, and iterative methods such as Conjugate Gradient and Bi-Conjugate Gradient.

Eigenvalues and Eigenvectors:
Eigen provides efficient algorithms for finding the eigenvalues and eigenvectors of a matrix. It supports a range of methods, including QR and Jacobi.

Decompositions:
Eigen provides efficient algorithms for decomposing matrices into simpler parts. It supports QR, LU, and SVD decompositions.

Error-free Numerical Derivatives:
Eigen provides efficient algorithms for computing numerical derivatives. It supports both forward and central difference methods.

Jacobians:
Eigen provides efficient algorithms for computing Jacobians. It supports both forward and central difference methods.

Sparse Matrices:
Eigen provides efficient algorithms for handling sparse matrices. It supports operations such as multiplication, addition, and subtraction.

Parallelism:
Eigen supports parallelism through OpenMP, which allows taking advantage of multi-core processors.

Data Structures:
Eigen provides efficient data structures such as vectors, matrices, and sparse matrices. It also provides a range of algorithms for manipulating these data structures.

Performance:
Eigen is optimized for performance, providing efficient algorithms and data structures. It is written in modern C++, making use of features such as templates and expression templates to reduce overhead.
The minimum technical requirements for Eigen software are:

- Operating System: Windows 7 or higher
- Processor: Intel Core 2 Duo or equivalent
- Memory: 4GB RAM
- Hard Drive: 8GB or higher
- Graphics: OpenGL 2.1 compatible
- Internet connection: Broadband connection with at least 256Kbps upload/download speed
image/svg+xmlBotttsPablo Stanleyhttps://bottts.com/Florian Körner
Oscar Y.

I recently used Eigen software for a project. The installation process was very straightforward and the software was easy to use. The software boasts a lot of features, but I found many of them to be redundant. The documentation was comprehensive and accurate. I found that the software ran very smoothly and was relatively bug-free. The support team was also very responsive when I needed help. The cost of the software is reasonable, but I think it could be improved with the addition of a few more features. So far, my experience with Eigen has been mostly positive.
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