Overview

Fast solvers for elliptic PDEs form a pillar of scientific computing. They enable detailed and accurate simulations of electromagnetic fields, fluid flows, biochemical processes, and much more. This textbook provides an introduction to fast solvers from the point of view of integral equation formulations, which lead to unparalleled accuracy and speed in many applications. The focus is on fast algorithms for handling dense matrices that arise in the discretization of integral operators, such as the fast multipole method and fast direct solvers. While the emphasis is on techniques for dense matrices, the text also describes how similar techniques give rise to linear complexity algorithms for computing the inverse or the LU factorization of a sparse matrix resulting from the direct discretization of an elliptic PDE. This is the first textbook to detail the active field of fast direct solvers, introducing readers to modern linear algebraic techniques for accelerating computations, such as randomized algorithms, interpolative decompositions, and data-sparse hierarchical matrix representations. Written with an emphasis on mathematical intuition rather than theoretical details, it is richly illustrated and provides pseudocode for all key techniques. Fast Direct Solvers for Elliptic PDEs is appropriate for graduate students in applied mathematics and scientific computing, engineers and scientists looking for an accessible introduction to integral equation methods and fast solvers, and researchers in computational mathematics who want to quickly catch up on recent advances in randomized algorithms and techniques for working with data-sparse matrices.

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9781611976038

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Per-Gunnar Martinsson is a professor of mathematics at The University of Texas at Austin, where he also holds an endowed chair in the Oden Institute for Computational Engineering and Sciences. He is a recipient of the SIAM 2017 Germund Dahlquist Prize, and his research concerns the development of fast algorithms for ubiquitous computational tasks in scientific computing and data sciences, with a recent focus on randomized methods in linear algebra, fast solvers for elliptic PDEs, linear complexity direct solvers, structured matrix computations, and high-order accurate methods for scattering and fluid problems.

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