This edition presents Jonathan Swift's most important Irish writings in both prose and verse, together with an introduction, head notes and annotations that shed new light on the full context and significance of each piece. Familiar works such as "Gulliver's Travels" and "A Tale of a Tub" acquire new and deeper meanings when considered within the Irish frameworks presented in the edition. Differing in noteworthy ways from the more traditional, canonical, Anglocentric picture conveyed by other published volumes, the Swift that emerges from these pages is a brilliant polemicist, popular satirist, political agitator, playful versifier, tormented Jeremiah, and Irish patriot.

This edition presents Jonathan Swift's most important Irish writings in both prose and verse, together with an introduction, head notes and annotations that shed new light on the full context and significance of each piece. Familiar works such as "Gulliver's Travels" and "A Tale of a Tub" acquire new and deeper meanings when considered within the Irish frameworks presented in the edition. Differing in noteworthy ways from the more traditional, canonical, Anglocentric picture conveyed by other published volumes, the Swift that emerges from these pages is a brilliant polemicist, popular satirist, political agitator, playful versifier, tormented Jeremiah, and Irish patriot.

Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It places careful emphasis on both the numerical formulation of the algorithm and its differential geometric abstraction--illustrating how good algorithms draw equally from the insights of differential geometry, optimization, and numerical analysis. Two more theoretical chapters provide readers with the background in differential geometry necessary to algorithmic development. In the other chapters, several well-known optimization methods such as steepest descent and conjugate gradients are generalized to abstract manifolds. The book provides a generic development of each of these methods, building upon the material of the geometric chapters. It then guides readers through the calculations that turn these geometrically formulated methods into concrete numerical algorithms. The state-of-the-art algorithms given as examples are competitive with the best existing algorithms for a selection of eigenspace problems in numerical linear algebra.

"Optimization Algorithms on Matrix Manifolds" offers techniques with broad applications in linear algebra, signal processing, data mining, computer vision, and statistical analysis. It can serve as a graduate-level textbook and will be of interest to applied mathematicians, engineers, and computer scientists.