A New Introduction to Classical Chinese

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This authoritative new work replaces the author's 1968 edition. It takes into account recent advances in scholarship with entirely revised notes on the text passages and improved and simplified explanations. The book's expansion also permits the author to continue the story beyond the fourth century B.C. and introduces the reader to the writings of the great Han Dynasty historian, Ssu-ma Ch'ien, who perfected a narrative style which became a model for future generations of Chinese writers.

Introduction to Linear Algebra 6/e (6版)

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Introduction to Linear Algebra 6/e 作者：Gilbert Strang ISBN：9786269708109 年份：2023 出版商：New Moon Education 頁數/規格：440頁/平裝單色 Description Linear algebra now rivals or surpasses calculus in importance for people working in quantitative fields of all kinds: engineers, scientists, economists and business people. Gilbert Strang has taught linear algebra at MIT for more than 50 years and the course he developed has become a model for teaching around the world. His video lectures on MIT OpenCourseWare have been viewed over ten million times and his twelve textbooks are popular with readers worldwide. This sixth edition of Professor Strang's most popular book, Introduction to Linear Algebra, introduces the ideas of independent columns and the rank and column space of a matrix early on for a more active start. Then the book moves directly to the classical topics of linear equations, fundamental subspaces, least squares, eigenvalues and singular values – in each case expressing the key idea as a matrix factorization. The final chapters of this edition treat optimization and learning from data: the most active application of linear algebra today. Everything is explained thoroughly in Professor Strang's characteristic clear style. It is sure to delight and inspire the delight and inspire the next generation of learners. Table of Contents 1 Vectors and Matrices 2 Solving Linear Equations Ax = b 3 The Four Fundamental Subspaces 4 Orthogonality 5 Determinants 6 Eigenvalues and Eigenvectors 7 The Singular Value Decomposition (SVD) 8 Linear Transformations 9 Linear Algebra in Optimization 10 Learning from Data