Lecture

Linear Independence and Basis

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COM-102: Advanced information, computation, communication II

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Description

This lecture covers the concepts of linear independence, basis, and rank of a matrix. It explains how to determine if a set of vectors is linearly independent, how to find a basis for a vector space, and the relationship between the rank of a matrix and the dimension of its column space. The lecture also discusses the properties of the dimension of a vector space and provides examples and exercises to reinforce the understanding of these fundamental concepts.

Instructors (2)

Bixio Rimoldi

Bixio Rimoldi received the Dipl. El.-Ing degree as well as the Dr. ès Sciences degree from the ETHZ, Switzerland. Since 1997, he holds a full professor position at the School of Computer and Communication Sciences at EPFL and he is the director of EPFL's Mobile Communications Laboratory (LCM). Prior to joining EPFL, he was in the faculty of the Electrical Engineering department of Washington University. In 1993, he received a US National Science Foundation Young Investigator Award. In 2000, he was elected to the grade of Fellow of the IEEE. During the period 2002-2009 he has been on the Board of Governors of the IEEE Information Theory Society, where he served in several offices including President. He was co-chairman with Bruce Hajek of the 1995 IEEE Information Theory Workshop on Information Theory, Multiple Access, and Queueing (St Louis, MO), and co-chairman with Jim Massey of the 2002 IEEE International Symposium in Information Theory (Lausanne, Switzerland). He has been a member of the editorial board of "Foundations and Trends on Communications and Information Theory," and was an editor of the European Transactions on Telecommunications. During 2005 and 2006 he was the Director of EPFL's Bachelor program in Communication Systems. His interests are in various aspects of digital communications, information theory, and software-defined radio.

Michael Christoph Gastpar

Michael Gastpar is a (full) Professor at EPFL. From 2003 to 2011, he was a professor at the University of California at Berkeley, earning his tenure in 2008. He received his Dipl. El.-Ing. degree from ETH Zürich, Switzerland, in 1997 and his MS degree from the University of Illinois at Urbana-Champaign, IL, USA, in 1999. He defended his doctoral thesis at EPFL on Santa Claus day, 2002. He was also a (full) Professor at Delft University of Technology, The Netherlands. His research interests are in network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. He is a Fellow of the IEEE. He is the co-recipient of the 2013 Communications Society & Information Theory Society Joint Paper Award. He was an Information Theory Society Distinguished Lecturer (2009-2011). He won an ERC Starting Grant in 2010, an Okawa Foundation Research Grant in 2008, an NSF CAREER award in 2004, and the 2002 EPFL Best Thesis Award. He has served as an Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11), and as Technical Program Committee Co-Chair for the 2010 International Symposium on Information Theory, Austin, TX.

Official source

Ontological neighbourhood

Mathematics

Algebra: Linear algebra

Related concepts (32)

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.

Diagonal matrix

In linear algebra, a diagonal matrix is a matrix in which the entries outside the main diagonal are all zero; the term usually refers to square matrices. Elements of the main diagonal can either be zero or nonzero. An example of a 2×2 diagonal matrix is , while an example of a 3×3 diagonal matrix is. An identity matrix of any size, or any multiple of it (a scalar matrix), is a diagonal matrix. A diagonal matrix is sometimes called a scaling matrix, since matrix multiplication with it results in changing scale (size).

Affine space

In mathematics, an affine space is a geometric structure that generalizes some of the properties of Euclidean spaces in such a way that these are independent of the concepts of distance and measure of angles, keeping only the properties related to parallelism and ratio of lengths for parallel line segments. In an affine space, there is no distinguished point that serves as an origin. Hence, no vector has a fixed origin and no vector can be uniquely associated to a point.

Complemented subspace

In the branch of mathematics called functional analysis, a complemented subspace of a topological vector space is a vector subspace for which there exists some other vector subspace of called its (topological) complement in , such that is the direct sum in the category of topological vector spaces. Formally, topological direct sums strengthen the algebraic direct sum by requiring certain maps be continuous; the result retains many nice properties from the operation of direct sum in finite-dimensional vector spaces.

Linear span

In mathematics, the linear span (also called the linear hull or just span) of a set S of vectors (from a vector space), denoted span(S), is defined as the set of all linear combinations of the vectors in S. For example, two linearly independent vectors span a plane. The linear span can be characterized either as the intersection of all linear subspaces that contain S, or as the smallest subspace containing S. The linear span of a set of vectors is therefore a vector space itself. Spans can be generalized to matroids and modules.

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