Linear Algebra
portes grátis
Linear Algebra
An Inquiry-Based Approach
Suzuki, Jeff
Taylor & Francis Ltd
05/2021
357
Dura
Inglês
9780367248963
15 a 20 dias
660
Descrição não disponível.
Introduction and Features. For the Student . . . and Teacher. Prerequisites. Suggested Sequences. 1. Tuples and Vectors. 1.1. Tuples. 1.2. Vectors. 1.3. Proofs. 1.4. Directed Distances. 1.5. Magnitude. 1.6. Direction. 1.7. Unit and Orthogonal Vectors. 2. Systems of Linear Equations. 2.1. Standard Form. 2.2. Solving Systems. 2.3. Coefficient Matrices. 2.4. Free and Basic Variables. 2.5. Computational Considerations. 2.6. Applications of Linear Algebra. 3. Transformations. 3.1. Geometric Transformations. 3.2. Vector Transformations. 3.3. The Transformation Matrix. 3.4. Domain, Codomain, and Range. 3.5. Discrete Time Models. 3.6. Linear Transformations. 3.7. Transformation Arithmetic. 3.8. Cryptography. 4. Matrix Algebra. 4.1. Scalar Multiplication. 4.2. Matrix Addition. 4.3. Matrix Multiplication. 4.4. Elementary Matrices. 4.5. More Transformations. 4.6. Matrix Inverses. 4.7. Complex Matrices. 5. Vector Spaces. 5.1. Vector Spaces. 5.2. Kernels and Null Spaces. 5.3. Span. 5.4. Linear Independence and Dependence. 5.5. Change of Basis. 5.6. Orthogonal Bases. 5.7. Normed Vector Spaces. 5.8. Inner Product Spaces. 5.9. Applications. 5.10. Least Squares. 6. Determinants. 6.1. Linear Equations. 6.2. Transformations. 6.3. Inverse. 6.4. The Determinant. 6.5. A Formula for the Determinant. 6.6. The Determinant Formula. 6.7. More Properties of the Determinant. 6.8. More Computations of the Determinant. 6.9. Use(lesses) of the Determinant. 6.10. Uses of the Determinant. 6.11. Permutations. 7. Eigenvalues and Eigenvectors. 7.1. More Transformations. 7.2. The Eigenproblem. 7.3. Finding Eigenvalues: Numerical Methods. 7.4. Eigenvalues and Eigenvectors for a 2 x 2 Matrix. 7.5. The Characteristic Equation. 7.6. Stochastic Matrices. 7.7. A Determinant-Free Approach. 7.8. Generalized Eigenvalues. 7.9. Symmetric Matrices. 7.10. Graphs. 8. Decomposition. 8.1. LU-Decomposition. 8.2. QR-Decomposition. 8.3. Eigendecompositions. 8.4. Singular Value Decomposition. 9. Extras. 9.1. Properties of Polynomials. 9.2. Complex Numbers. 9.3. Mod-N Arithmetic. 9.4. Polar Coordinates. Bibliography. Index.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Reduced Row Echelon Form;Algorithm design;Elementary Row Operation;Divide and conquor algorithms;Row Reducing;Greedy algorithms;Row Echelon Form;Dynamic programming algorithms;Finding Integer Solutions;Graph Algorithms;Row Operation;Matrix algebra;Linear Transformation;Vector spaces;Row Pivot;Transformations;Steady State Vector;Tuples;QR Decomposition;Linear equations;Generalized Eigenvector;Left Inverse;Transition Matrix;Lower Triangular Matrix;Integer Solutions;Hill Cipher;Row Interchanges;Triangular Matrix;Orthogonal Matrices;Orthonormal Basis;Diagonal Matrix;Nondecreasing Order;Eigenvalue Eigenvector Pair;Gram Schmidt Process;Transformation Matrix
Introduction and Features. For the Student . . . and Teacher. Prerequisites. Suggested Sequences. 1. Tuples and Vectors. 1.1. Tuples. 1.2. Vectors. 1.3. Proofs. 1.4. Directed Distances. 1.5. Magnitude. 1.6. Direction. 1.7. Unit and Orthogonal Vectors. 2. Systems of Linear Equations. 2.1. Standard Form. 2.2. Solving Systems. 2.3. Coefficient Matrices. 2.4. Free and Basic Variables. 2.5. Computational Considerations. 2.6. Applications of Linear Algebra. 3. Transformations. 3.1. Geometric Transformations. 3.2. Vector Transformations. 3.3. The Transformation Matrix. 3.4. Domain, Codomain, and Range. 3.5. Discrete Time Models. 3.6. Linear Transformations. 3.7. Transformation Arithmetic. 3.8. Cryptography. 4. Matrix Algebra. 4.1. Scalar Multiplication. 4.2. Matrix Addition. 4.3. Matrix Multiplication. 4.4. Elementary Matrices. 4.5. More Transformations. 4.6. Matrix Inverses. 4.7. Complex Matrices. 5. Vector Spaces. 5.1. Vector Spaces. 5.2. Kernels and Null Spaces. 5.3. Span. 5.4. Linear Independence and Dependence. 5.5. Change of Basis. 5.6. Orthogonal Bases. 5.7. Normed Vector Spaces. 5.8. Inner Product Spaces. 5.9. Applications. 5.10. Least Squares. 6. Determinants. 6.1. Linear Equations. 6.2. Transformations. 6.3. Inverse. 6.4. The Determinant. 6.5. A Formula for the Determinant. 6.6. The Determinant Formula. 6.7. More Properties of the Determinant. 6.8. More Computations of the Determinant. 6.9. Use(lesses) of the Determinant. 6.10. Uses of the Determinant. 6.11. Permutations. 7. Eigenvalues and Eigenvectors. 7.1. More Transformations. 7.2. The Eigenproblem. 7.3. Finding Eigenvalues: Numerical Methods. 7.4. Eigenvalues and Eigenvectors for a 2 x 2 Matrix. 7.5. The Characteristic Equation. 7.6. Stochastic Matrices. 7.7. A Determinant-Free Approach. 7.8. Generalized Eigenvalues. 7.9. Symmetric Matrices. 7.10. Graphs. 8. Decomposition. 8.1. LU-Decomposition. 8.2. QR-Decomposition. 8.3. Eigendecompositions. 8.4. Singular Value Decomposition. 9. Extras. 9.1. Properties of Polynomials. 9.2. Complex Numbers. 9.3. Mod-N Arithmetic. 9.4. Polar Coordinates. Bibliography. Index.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Reduced Row Echelon Form;Algorithm design;Elementary Row Operation;Divide and conquor algorithms;Row Reducing;Greedy algorithms;Row Echelon Form;Dynamic programming algorithms;Finding Integer Solutions;Graph Algorithms;Row Operation;Matrix algebra;Linear Transformation;Vector spaces;Row Pivot;Transformations;Steady State Vector;Tuples;QR Decomposition;Linear equations;Generalized Eigenvector;Left Inverse;Transition Matrix;Lower Triangular Matrix;Integer Solutions;Hill Cipher;Row Interchanges;Triangular Matrix;Orthogonal Matrices;Orthonormal Basis;Diagonal Matrix;Nondecreasing Order;Eigenvalue Eigenvector Pair;Gram Schmidt Process;Transformation Matrix
