Exploiting Hidden Structure in Matrix Computations: Algorithms and Applications

Focusing on special matrices and matrices which are in some sense `near' to structured matrices, this volume covers a broad range of topics of current interest in numerical linear algebra. Exploitation of these less obvious structural properties can be of great importance in the design of efficient numerical methods, for example algorithms for matrices with low-rank block structure, matrices with decay, and structured tensor computations. Applications range from quantum chemistry to queuing theory.

Structured matrices arise frequently in applications. Examples include banded and sparse matrices, Toeplitz-type matrices, and matrices with semi-separable or quasi-separable structure, as well as Hamiltonian and symplectic matrices. The associated literature is enormous, and many efficient algorithms have been developed for solving problems involving such matrices.

The text arose from a C.I.M.E. course held in Cetraro (Italy) in June 2015 which aimed to present this fast growing field to young researchers, exploiting the expertise of five leading lecturers with different theoretical and application perspectives.

Provides a detailed treatment of the emerging field of hidden or approximate-structured matrix problems Offers different theoretical and application perspectives in a thorough presentation by leading figures in this area Gives rich pointers to the state-of-the-art literature on the subject Includes supplementary material: sn.pub/extras

Contenu

Preface.- Charles F. Van Loan : Structured Matrix Problems from Tensors.- Dario A. Bini : Matrix Structures in Queuing Models.-. Jonas Ballani and Daniel Kressner : Matrices with Hierarchical Low-Rank Structures.- Michele Benzi: Localization in Matrix Computations: Theory and Applications.- Munthe-Kaas: Groups and Symmetries in Numerical Linear Algebra.