# Difference between revisions of "TNT"

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− | The Template Numerical Toolkit (TNT) is a series of C++ templates that implement matrices and vectors. You can get the code and documentation from | + | The Template Numerical Toolkit (TNT) is a series of C++ templates that implement matrices and vectors. You can get the code and documentation from [http://math.nist.gov/tnt]. The most useful basic data type is <code>TNT::Array2D</code>, which is the preferred matrix data type. <code>TNT::Array1D</code> is occasionally useful for vectors that you are using with TNT code. Operators and []s are overloaded for matrices like you would expect, and an the templates handle memory allocation and sizing. The associated JAMA templates (same URL) provide Cholesky, Eigenvalue, LU, QR, and SVD decompositions. |

The nice things about this library are that it is simple, free, and implemented entirely as templates (so you just specify -I/path/to/tnt and include the headers). On the down side, it's a bit limited in what it provides, and bjbrown has encountered a couple of bugs. He thinks they were in QR and Eigenvalue decompositions, and if they aren't fix in the official distro, e-mail him for the fixes. | The nice things about this library are that it is simple, free, and implemented entirely as templates (so you just specify -I/path/to/tnt and include the headers). On the down side, it's a bit limited in what it provides, and bjbrown has encountered a couple of bugs. He thinks they were in QR and Eigenvalue decompositions, and if they aren't fix in the official distro, e-mail him for the fixes. |

## Revision as of 13:29, 10 April 2006

The Template Numerical Toolkit (TNT) is a series of C++ templates that implement matrices and vectors. You can get the code and documentation from [1]. The most useful basic data type is `TNT::Array2D`

, which is the preferred matrix data type. `TNT::Array1D`

is occasionally useful for vectors that you are using with TNT code. Operators and []s are overloaded for matrices like you would expect, and an the templates handle memory allocation and sizing. The associated JAMA templates (same URL) provide Cholesky, Eigenvalue, LU, QR, and SVD decompositions.

The nice things about this library are that it is simple, free, and implemented entirely as templates (so you just specify -I/path/to/tnt and include the headers). On the down side, it's a bit limited in what it provides, and bjbrown has encountered a couple of bugs. He thinks they were in QR and Eigenvalue decompositions, and if they aren't fix in the official distro, e-mail him for the fixes.

Oddly, the library doesn't transpose matrices. It also doesn't invert them. Here's the simplest matrix inversion (and determinant) code:

#include <assert.h> #include <tnt_array1d.h> #include <tnt_array2d.h> #include <jama_lu.h> TNT::Array2D<float> invert(TNT::Array2D<float> &M) { assert(M.dim1() == M.dim2()); // square matrices only please // create identity matrix TNT::Array2D<float> id(M.dim1(), M.dim2(), 0); for (int i = 0; i < M.dim1(); i++) id[i][i] = 1; // solve for inverse with LU decomposition JAMA::LU lu(M); assert(LU.det() > THRESHOLD); // non-singularity return lu.solve(id); }