Lecture

Eigen Library for Linear Algebra

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

In course

MATH-458: Programming concepts in scientific computing

The aim of this course is to provide the background in scientific computing. The class includes a brief introduction to basic programming in c++, it then focuses on object-oriented programming and c++ specific programming techniques. Typical class hierarch ...

Description

This lecture covers the Eigen library for linear algebra, focusing on vectors, matrices, arrays, and their sizes, initialization, vector types, dynamic and static sizes, linear algebra operations, solving linear systems, memory management, reshaping, and sparse matrices. It also discusses Eigen::Map for wrapping memory, reshaping, and extracting block matrices. The lecture concludes with arrays, per-component operations, documentation, and the key takeaway message emphasizing Eigen's flexibility and efficiency in C++.

Instructor

Guillaume Anciaux

ResearchResearch Topics My research can be decomposed in three main blocks:First, the main goal of my current research is the physical description of contact interactions. I performed numerous studies of this topic using different models from the continuum scale down to atomistic scale. Since atomic phenomena and macroscopic observations are linked, the physics of contact needs the simultaneous consideration of several scales. For instance, plasticity is a theory defined for the macroscopic scale whereas t

Official source

Ontological neighbourhood

Mathematics

Algebra: Linear algebra

Related concepts (31)

Block matrix

In mathematics, a block matrix or a partitioned matrix is a matrix that is interpreted as having been broken into sections called blocks or submatrices. Intuitively, a matrix interpreted as a block matrix can be visualized as the original matrix with a collection of horizontal and vertical lines, which break it up, or partition it, into a collection of smaller matrices. Any matrix may be interpreted as a block matrix in one or more ways, with each interpretation defined by how its rows and columns are partitioned.

Linear algebra

Linear algebra is the branch of mathematics concerning linear equations such as: linear maps such as: and their representations in vector spaces and through matrices. Linear algebra is central to almost all areas of mathematics. For instance, linear algebra is fundamental in modern presentations of geometry, including for defining basic objects such as lines, planes and rotations. Also, functional analysis, a branch of mathematical analysis, may be viewed as the application of linear algebra to spaces of functions.

Hadamard product (matrices)

In mathematics, the Hadamard product (also known as the element-wise product, entrywise product or Schur product) is a binary operation that takes in two matrices of the same dimensions and returns a matrix of the multiplied corresponding elements. This operation can be thought as a "naive matrix multiplication" and is different from the matrix product. It is attributed to, and named after, either French-Jewish mathematician Jacques Hadamard or German-Jewish mathematician Issai Schur.

Gamma matrices

In mathematical physics, the gamma matrices, also called the Dirac matrices, are a set of conventional matrices with specific anticommutation relations that ensure they generate a matrix representation of the Clifford algebra It is also possible to define higher-dimensional gamma matrices. When interpreted as the matrices of the action of a set of orthogonal basis vectors for contravariant vectors in Minkowski space, the column vectors on which the matrices act become a space of spinors, on which the Clifford algebra of spacetime acts.

Invertible matrix

In linear algebra, an n-by-n square matrix A is called invertible (also nonsingular, nondegenerate or (rarely used) regular), if there exists an n-by-n square matrix B such that where In denotes the n-by-n identity matrix and the multiplication used is ordinary matrix multiplication. If this is the case, then the matrix B is uniquely determined by A, and is called the (multiplicative) inverse of A, denoted by A−1. Matrix inversion is the process of finding the matrix B that satisfies the prior equation for a given invertible matrix A.

Related lectures (64)

Matrix Operations: Definitions and Properties

Covers the definitions and properties of matrices, including matrix operations and determinants.

Characteristic Polynomials and Similar Matrices

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Eigenspaces: Definitions and Examples MOOC: Linear Algebra (Part 2)

Introduces eigenspaces in linear algebra through definitions and practical examples of determining eigenspaces for matrices.

Linear Equations: Vectors and Matrices

Covers linear equations, vectors, and matrices, exploring their fundamental concepts and applications.

Eigen and Pybind Libraries