Numerical Linear Algebra

2017 ◽  
Author(s):  
William Layton ◽  
Myron Sussman
Keyword(s):  
Linear Algebra ◽  
Undergraduate Students ◽  
Eigenvalue Problems ◽  
Matrix Theory ◽  
Direct Methods ◽  
Numerical Linear Algebra ◽  
Good Choice ◽  
Computational Mathematics ◽  
Diverse Backgrounds ◽  
Sparse Systems

This textbook was designed for senior undergraduates in mathematics, engineering and the sciences with diverse backgrounds and goals. It presents modern tools from numerical linear algebra with supporting theory along with examples and exercises, both theoretical and computational with MATLAB. The major topics of numerical linear algebra covered are direct methods for solving linear systems, iterative methods for large and sparse systems (including the conjugate gradient method) and eigenvalue problems. Basic linear algebra (of the type taken in the engineering curriculum) is assumed. Further matrix theory is developed in a self-contained way when needed to expalin why methods work and how they might fail. This book is intended for a one term course for undergraduate students in applied and computational mathematics, the sciences, engineering, computer science, financial mathematics and actuarial science. It is also a good choice for a beginning graduate level course for students who will use the numerical methods to solve problems in their own areas.

Communication lower bounds and optimal algorithms for numerical linear algebra

Acta Numerica ◽  
2014 ◽  
Vol 23 ◽  
pp. 1-155 ◽  
Author(s):  
G. Ballard ◽  
E. Carson ◽  
J. Demmel ◽  
M. Hoemmen ◽  
N. Knight ◽  
...  
Keyword(s):  
Linear Algebra ◽  
Lower Bounds ◽  
Krylov Subspace ◽  
Sparse Matrices ◽  
Arithmetic Operation ◽  
Matrix Multiplication ◽  
Direct Methods ◽  
Numerical Linear Algebra ◽  
Theory And Practice ◽  
Large Speed

The traditional metric for the efficiency of a numerical algorithm has been the number of arithmetic operations it performs. Technological trends have long been reducing the time to perform an arithmetic operation, so it is no longer the bottleneck in many algorithms; rather, communication, or moving data, is the bottleneck. This motivates us to seek algorithms that move as little data as possible, either between levels of a memory hierarchy or between parallel processors over a network. In this paper we summarize recent progress in three aspects of this problem. First we describe lower bounds on communication. Some of these generalize known lower bounds for dense classical (O(n3)) matrix multiplication to all direct methods of linear algebra, to sequential and parallel algorithms, and to dense and sparse matrices. We also present lower bounds for Strassen-like algorithms, and for iterative methods, in particular Krylov subspace methods applied to sparse matrices. Second, we compare these lower bounds to widely used versions of these algorithms, and note that these widely used algorithms usually communicate asymptotically more than is necessary. Third, we identify or invent new algorithms for most linear algebra problems that do attain these lower bounds, and demonstrate large speed-ups in theory and practice.

Matrix Completions, Moments, and Sums of Hermitian Squares

2011 ◽  
Author(s):  
Mihály Bakonyi ◽  
Hugo J. Woerdeman
Keyword(s):  
Linear Algebra ◽  
Undergraduate Students ◽  
Complex Analysis ◽  
Measure Theory ◽  
Complex Function ◽  
Complex Function Theory ◽  
Extensive Discussion ◽  
The Subject ◽  
Processing Control ◽  
Developing Area

Intensive research in matrix completions, moments, and sums of Hermitian squares has yielded a multitude of results in recent decades. This book provides a comprehensive account of this quickly developing area of mathematics and applications and gives complete proofs of many recently solved problems. With MATLAB codes and more than two hundred exercises, the book is ideal for a special topics course for graduate or advanced undergraduate students in mathematics or engineering, and will also be a valuable resource for researchers. Often driven by questions from signal processing, control theory, and quantum information, the subject of this book has inspired mathematicians from many subdisciplines, including linear algebra, operator theory, measure theory, and complex function theory. In turn, the applications are being pursued by researchers in areas such as electrical engineering, computer science, and physics. The book is self-contained, has many examples, and for the most part requires only a basic background in undergraduate mathematics, primarily linear algebra and some complex analysis. The book also includes an extensive discussion of the literature, with close to six hundred references from books and journals from a wide variety of disciplines.

scholarly journals Some algorithms for maximum volume and cross approximation of symmetric semidefinite matrices

2021 ◽  
Author(s):  
Stefano Massei
Keyword(s):  
Computer Science ◽  
Recent Work ◽  
Linear Algebra ◽  
Optimization Problems ◽  
Approximation Error ◽  
Positive Semidefinite ◽  
Numerical Linear Algebra ◽  
Maximum Volume ◽  
Deterministic Algorithms ◽  
Principal Submatrix

AbstractVarious applications in numerical linear algebra and computer science are related to selecting the $$r\times r$$ r × r submatrix of maximum volume contained in a given matrix $$A\in \mathbb R^{n\times n}$$ A ∈ R n × n . We propose a new greedy algorithm of cost $$\mathcal O(n)$$ O ( n ) , for the case A symmetric positive semidefinite (SPSD) and we discuss its extension to related optimization problems such as the maximum ratio of volumes. In the second part of the paper we prove that any SPSD matrix admits a cross approximation built on a principal submatrix whose approximation error is bounded by $$(r+1)$$ ( r + 1 ) times the error of the best rank r approximation in the nuclear norm. In the spirit of recent work by Cortinovis and Kressner we derive some deterministic algorithms, which are capable to retrieve a quasi optimal cross approximation with cost $$\mathcal O(n^3)$$ O ( n 3 ) .

scholarly journals Numerical Linear Algebra in Signal Processing Applications

2007 ◽  
Vol 2007 (1) ◽  
Author(s):  
Nicola Mastronardi ◽  
Gene H Golub ◽  
Shivkumar Chandrasekaran ◽  
Marc Moonen ◽  
Paul Van Dooren ◽  
...  
Keyword(s):  
Signal Processing ◽  
Linear Algebra ◽  
Numerical Linear Algebra
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