Matrices are two dimensional named data objects (Igor supports up to four dimensions.) You can perform basic arithmetic operations on matrices using standard data assignment statements. For example:

You can perform matrix multiplication and matrix dot product with a natural syntax using MatrixOP. For example:

The following example computes element by element multiplication of matrix A with matrix B from which we subtract the inverse of matrix C times the diagonal matrix created from the array D:

MatrixOP supports array based operations such as Fourier Transforms, Chirp-Z transforms, convolutions and correlations. For example, you can compute circular or acausal convolutions using cascaded transforms and array multiplications as in the following lines:

Obviously you can get the same results using the more compact syntax:

The following is a list of functions supported under MatrixOP:

Linear Algebra Operations

Igor Pro® includes a group of operations and functions for linear algebra applications. For convenience their names start with the word "Matrix". Additional matrix math features are grouped under image operations.

Igor uses the industry-tested LAPACK library for many linear algebra operations. Following is a list of the more common MatrixXXX operations:

Other Matrix Operations:

MatrixConvolve: convolves a small 2D kernel with a larger destination matrix usually for image processing applications.

MatrixFilter: contains a variety of options for filtering matrix data usually for image processing. Some of the built-in filters include averaging, edge finding, Gaussian blur, gradients, median, sharpen and more.