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Computational algebraic geometry
Author:
ISBN: 1107138744 0511078773 9786612389399 1282389394 0511643519 0511205589 0511566581 0511756321 0511077203 9780511078774 9780511756320 9780521829649 052182964X 9781107138742 6612389397 9781282389397 9780511643514 9780511205583 9780511566585 9780511077203 9780521536509 0521536502 Year: 2003 Publisher: Cambridge, UK New York Cambridge University Press

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Abstract

The interplay between algebra and geometry is a beautiful (and fun!) area of mathematical investigation. Advances in computing and algorithms make it possible to tackle many classical problems in a down-to-earth and concrete fashion. This opens wonderful new vistas and allows us to pose, study and solve problems that were previously out of reach. Suitable for graduate students, the objective of this 2003 book is to bring advanced algebra to life with lots of examples. The first chapters provide an introduction to commutative algebra and connections to geometry. The rest of the book focuses on three active areas of contemporary algebra: Homological Algebra (the snake lemma, long exact sequence inhomology, functors and derived functors (Tor and Ext), and double complexes); Algebraic Combinatorics and Algebraic Topology (simplicial complexes and simplicial homology, Stanley-Reisner rings, upper bound theorem and polytopes); and Algebraic Geometry (points and curves in projective space, Riemann-Roch, Cech cohomology, regularity).


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Algebraic Foundations for Applied Topology and Data Analysis
Author:
ISBN: 3031066634 3031066642 Year: 2022 Publisher: Cham Springer International Publishing AG

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Algebraic Foundations for Applied Topology and Data Analysis
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ISBN: 9783031066641 9783031066634 9783031066658 9783031066665 Year: 2022 Publisher: Cham Springer International Publishing

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This book gives an intuitive and hands-on introduction to Topological Data Analysis (TDA). Covering a wide range of topics at levels of sophistication varying from elementary (matrix algebra) to esoteric (Grothendieck spectral sequence), it offers a mirror of data science aimed at a general mathematical audience. The required algebraic background is developed in detail. The first third of the book reviews several core areas of mathematics, beginning with basic linear algebra and applications to data fitting and web search algorithms, followed by quick primers on algebra and topology. The middle third introduces algebraic topology, along with applications to sensor networks and voter ranking. The last third covers key contemporary tools in TDA: persistent and multiparameter persistent homology. Also included is a user's guide to derived functors and spectral sequences (useful but somewhat technical tools which have recently found applications in TDA), and an appendix illustrating a number of software packages used in the field. Based on a course given as part of a masters degree in statistics, the book is appropriate for graduate students. .

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