Linear Programming.Foundations and Extensions.4Ed.pdf。Robert J. Vanderbei。英文第四版

Calculus, Vol. 1 One-Variable Calculus, with an Introduction to to Linear Algebra by Tom M. Apostol

线性链-crf PyTorch 中的线性链 CRF。 解释此实现的博客文章： : 例子 检查bilstm_crf.py和main.py 。 依赖关系 torch>=0.4.1 ：您可以通过运行pip3 install torch安装它 执照 麻省理工学院。 有关更多详细信息，请参阅文件。

Linear Algebra with Applications by Leon Steven

中文翻译Introduction to Linear Algebra, 5th Edition 6.5节　仅用于交流学习！

Elementary Linear Algebra With Applications, 9Th Edition -

Elementary Linear Algebra, 8th Edition by Ron Larson | 467 pages Table of Contents 1. SYSTEMS OF LINEAR EQUATIONS. Introduction to Systems of Equations. Gaussian Elimination and Gauss-Jordan Elimination. Applications of Systems of Linear Equations. 2. MATRICES. Operations with Matrices. Properties of Matrix Operations. The Inverse of a Matrix. Elementary Matrices. Markov Chains. Applications of Matrix Operations. 3. DETERMINANTS. The Determinant of a Matrix. Evaluation of a Determinant Using Elementary Operations. Properties of Determinants. Applications of Determinants. 4. VECTOR SPACES. Vectors in Rn. Vector Spaces. Subspaces of Vector Spaces. Spanning Sets and Linear Independence. Basis and Dimension. Rank of a Matrix and Systems of Linear Equations. Coordinates and Change of Basis. Applications of Vector Spaces. 5. INNER PRODUCT SPACES. Length and Dot Product in Rn. Inner Product Spaces. Orthogonal Bases: Gram-Schmidt Process. Mathematical Models and Least Squares Analysis. Applications of Inner Product Spaces. 6. LINEAR TRANSFORMATIONS. Introduction to Linear Transformations. The Kernel and Range of a Linear Transformation. Matrices for Linear Transformations. Transition Matrices and Similarity. Applications of Linear Transformations. 7. EIGENVALUES AND EIGENVECTORS. Eigenvalues and Eigenvectors. Diagonalization. Symmetric Matrices and Orthogonal Diagonalization. Applications of Eigenvalues and Eigenvectors. 8. COMPLEX VECTOR SPACES (online). Complex Numbers. Conjugates and Division of Complex Numbers. Polar Form and Demoivre’s Theorem. Complex Vector Spaces and Inner Products. Unitary and Hermitian Spaces. 9. LINEAR PROGRAMMING (online). Systems of Linear Inequalities. Linear Programming Involving Two Variables. The Simplex Method: Maximization. The Simplex Method: Minimization. The Simplex Method: Mixed Constraints. 10. NUMERICAL METHODS (online). Gaussian Elimination with Partial Pivoting. Iterative Methods for Solving Linear Systems. Power Method for Approximating Eigenvalues. Applications of Numerical Methods.

a very good book for linear system throry

经典教材，讲述线性和非线性方面的优化问题。

This book is intended as a text covering the central concepts of practical optimization techniques. It is designed for either self-study by professionals or classroom work at the undergraduate or graduate level for students who have a technical background in engineering, mathematics, or science. Like the field of optimization itself, which involves many classical disciplines, the book should be useful to system analysts, operations researchers, numerical analysts, management scientists, and other specialists from the host of disciplines from which practical optimization applications are drawn. The prerequisites for convenient use of the book are relatively modest; the prime requirement being some familiarity with introductory elements of linear algebra. Certain sections and developments do assume some knowledge of more advanced concepts of linear algebra, such as eigenvector analysis, or some background in sets of real numbers, but the text is structured so that the mainstream of the development can be faithfully pursued without reliance on this more advanced background material.