Part I - Introduction
1.7 Building and Deploying NMath Applications
Part II - The Core Namespace
Chapter 3. Complex Number Types
3.2 Value Operations on Complex Numbers
3.3 Logical Operations on Complex Numbers
3.4 Arithmetic Operations on Complex Numbers
3.5 Functions of Complex Numbers
5.3 Value Operations on Vectors
5.4 Logical Operations on Vectors
5.5 Arithmetic Operations on Vectors
6.3 Value Operations on Matrices
6.4 Logical Operations on Matrices
6.5 Arithmetic Operations on Matrices
Chapter 7. Solutions of Linear Systems
7.2 Creating LU Factorizations
8.2 Creating Least Squares Solutions
8.3 Using Least Squares Solutions
8.4 Nonnegative Least Squares Solutions
Chapter 9. Random Number Generators
9.1 Scalar Random Number Generators
9.2 Vectorized Random Number Generators
Chapter 10. Fourier Transforms, Convolution and Correlation
10.2 Convolution and Correlation
Chapter 11. Discrete Wavelet Transforms
11.2 Computing Discrete Wavelet Transforms
12.2 Adding Data to Histograms
12.3 Value Operations of Histograms
14.3 Savitzky-Golay Peak Finding
Part III - The Matrix Namespace
Chapter 16. The Matrix Namespace
Chapter 17. Structured Sparse Matrix Types
17.1 Lower Triangular Matrices
17.2 Upper Triangular Matrices
17.7 Symmetric Banded Matrices
17.8 Hermitian Banded Matrices
Chapter 18. Using The Structured Sparse Matrix Classes
18.2 Value Operations on Matrices
18.3 Logical Operations on Matrices
18.4 Arithmetic Operations on Matrices
Chapter 19. General Sparse Vectors and Matrices
19.3 Sparse Matrix Factorizations
Chapter 20. Structured Sparse Matrix Factorizations
21.2 Singular Value Decompositions
Chapter 22. Least Squares Solutions
22.1 Ordinary Least Squares Methods
22.2 Creating Ordinary Least Squares Objects
22.3 Using Ordinary Least Squares Objects
22.5 Iteratively Reweighted Least Squares
Chapter 23. EigenValue Problems
23.2 Using the Eigenvalue Classes
23.3 Using the Eigenvalue Server Classes
Part IV - The Analysis Namespace
Chapter 24. The Analysis Namespace
Chapter 25. Encapsulating Multivariate Functions
25.1 Creating Multivariate Functions
25.2 Evaluating Multivariate Functions
25.3 Algebraic Manipulation of Multivariate Functions
Chapter 26. Minimizing Univariate Functions
26.2 Minimizing Functions Without Calculating the Derivative
26.3 Minimizing Derivable Functions
Chapter 27. Minimizing Multivariate Functions
27.1 Minimizing Functions Without Calculating the Derivative
27.2 Minimizing Derivable Functions
Chapter 28. Simulated Annealing
28.3 Minimizing Functions by Simulated Annealing
Chapter 29. Linear Programming
29.1 Encapsulating LP Problems
Chapter 30. Nonlinear and Quadratic Programming
30.1 Objective and Constraint Function Classes
30.4 Constrained Least Squares
Chapter 31. Fitting Polynomials
31.1 Creating PolynomialLeastSquares
31.2 Properties of PolynomialLeastSquares
Chapter 32. Nonlinear Least Squares
32.1 Nonlinear Least Squares Interfaces
32.2 Trust-Region Minimization
32.3 Levenberg-Marquardt Minimization
32.4 Nonlinear Least Squares Curve Fitting
32.5 Nonlinear Least Squares Surface Fitting
Chapter 33. Finding Roots of Univariate Functions
33.1 Finding Function Roots Without Calculating the Derivative
33.2 Finding Function Roots of Derivable Functions
Chapter 34. Integrating Multivariable Functions
34.1 Creating TwoVariableIntegrators
34.2 Integrating Functions of Two Variables
Chapter 35. Differential Equations
35.1 Encapsulating Differential Equations
35.2 Solving Differential Equations
Part V - Miscellaneous Topics
Chapter 37. Database Integration
37.1 Creating ADO.NET Objects from Vectors and Matrices
37.2 Creating Vector and Matrices from ADO.NET Objects
39.9 Overriding Bridge Routing