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Lecture

Vectors and Tensors: Mathematical Preliminaries and Spectral Decomposition

Description

This lecture covers mathematical preliminaries related to eigenvectors and eigenvalues, emphasizing the spectral decomposition of matrices. It also discusses vector and tensor fields, including quantities varying with spatial position and the need for spatial and time derivatives. The lecture further explores the orthogonality of eigenvectors and the representation of matrices in a basis set aligned with the eigenvectors.

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Instructors (3)

William Curtin

John Martin Kolinski

Dr. Kolinski studied Applied Mathematics (Sc.M.) and Applied Physics (Ph.D.) at Harvard University, completing a PhD under the supervision of L. Mahadevan and Shmuel Rubinstein on the role of air in droplet impact. John did his post-doc at the Hebrew University of Jerusalem in Israel supported by the Fulbright post-doctoral fellowship. At HUJI, he worked on interfacial instabilities in soft matter in the labs of Eran Sharon and Jay Fineberg. John continues his research into interfacial mechanics at EPFL in EMSI, his newly founded laboratory for the study of Engineering Mechanics of Soft Interfaces.

John Botsis

John (Ioannis) Botsis obtained his diplôme in civil engineering at the University of Patras, Greece in 1979. He continued his education at Case Institute of Technology in Cleveland Ohio/USA, where he received his MS and Ph.D. in 1984. After two years at the research center for national defense in Athens he was nominated assistant professor at the University of Illinois in Chicago, associate in 1991 and full professor in 1995. In 1996, he was nominated professor of solids and structural mechanics at the EPFL. At EPFL he teaches mechanics of structures and mechanics of continuous media´ at the bachelors level and Fracture mechanics at the masters and doctoral levels. His research covers the mechanics of solids and structures, fracture mechanics and micromechanics of polymers, metals and their composites as well as biomechanics. He is also actively involved in full-filed optical methods for surface strain measurements as well as internal strain measurements using fiber Bragg grating sensors, aimed at characterizing micromechanics of fracture, residual strains and strain distribution in composite laminates for structural monitoring. Funding for his research comes from the Swiss National Science Foundation, State Secretariat for Education and Research and Swiss industry. He retired on February 28, 2020.

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Related concepts (32)

Eigenvalues and eigenvectors

In linear algebra, an eigenvector (ˈaɪgənˌvɛktər) or characteristic vector of a linear transformation is a nonzero vector that changes at most by a constant factor when that linear transformation is applied to it. The corresponding eigenvalue, often represented by , is the multiplying factor. Geometrically, a transformation matrix rotates, stretches, or shears the vectors it acts upon. The eigenvectors for a linear transformation matrix are the set of vectors that are only stretched, with no rotation or shear.

Eigendecomposition of a matrix

In linear algebra, eigendecomposition is the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors. Only diagonalizable matrices can be factorized in this way. When the matrix being factorized is a normal or real symmetric matrix, the decomposition is called "spectral decomposition", derived from the spectral theorem. Eigenvalue, eigenvector and eigenspace A (nonzero) vector v of dimension N is an eigenvector of a square N × N matrix A if it satisfies a linear equation of the form for some scalar λ.

Generalized eigenvector

In linear algebra, a generalized eigenvector of an matrix is a vector which satisfies certain criteria which are more relaxed than those for an (ordinary) eigenvector. Let be an -dimensional vector space and let be the matrix representation of a linear map from to with respect to some ordered basis. There may not always exist a full set of linearly independent eigenvectors of that form a complete basis for . That is, the matrix may not be diagonalizable.

Eigenvalue algorithm

In numerical analysis, one of the most important problems is designing efficient and stable algorithms for finding the eigenvalues of a matrix. These eigenvalue algorithms may also find eigenvectors. Eigenvalues and eigenvectors and Generalized eigenvector Given an n × n square matrix A of real or complex numbers, an eigenvalue λ and its associated generalized eigenvector v are a pair obeying the relation where v is a nonzero n × 1 column vector, I is the n × n identity matrix, k is a positive integer, and both λ and v are allowed to be complex even when A is real.

Schur decomposition

In the mathematical discipline of linear algebra, the Schur decomposition or Schur triangulation, named after Issai Schur, is a matrix decomposition. It allows one to write an arbitrary complex square matrix as unitarily equivalent to an upper triangular matrix whose diagonal elements are the eigenvalues of the original matrix. The Schur decomposition reads as follows: if A is an n × n square matrix with complex entries, then A can be expressed as where Q is a unitary matrix (so that its inverse Q−1 is also the conjugate transpose Q* of Q), and U is an upper triangular matrix, which is called a Schur form of A.

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