Sizes of Breakpoint Vectors and Table Data

The following constraints apply to the sizes of breakpoint vectors and table data associated with lookup table blocks:

To illustrate the second constraint, consider the following vectors of input and output values that create the relationship in the plot.

Vector of input values:   [-3 -2 -1  0 1 2 3]
Vector of output values:  [-3 -1  0 -1 0 1 3]

In this example, the input and output data are the same size (1-by-7), making the data consistently dimensioned for a 1-D lookup table.

The following input and output values define the 2-D lookup table that is graphically shown.

Row index input values:    [1 2 3]
Column index input values: [1 2 3 4]
Table data:                [11 12 13 14; 21 22 23 24; 31 32 33 34]

In this example, the sizes of the vectors representing the row and column indices are 1-by-3 and 1-by-4, respectively. Consequently, the output table must be of size 3-by-4 for consistent dimensions.

Monotonicity of Breakpoints

The first stage of a table lookup operation involves relating inputs to the breakpoints. The search algorithm requires that input breakpoint sets be strictly monotonically increasing, that is, each successive element is greater than its preceding element. For example, the vector

A = [0  0.5  1  1.9  2.1  3]

is a valid breakpoint vector as each element is larger than its predecessors.

Formulation of Evenly Spaced Breakpoints

You can represent evenly spaced breakpoints in a data set by using one of these methods.

Because floating-point data types cannot precisely represent some numbers, the second formulation works better for double and single. For example, use 1 + (0.02 * [0:450]) instead of [1:0.02:10]. For a list of lookup table blocks that support evenly spaced breakpoints, see Summary of Lookup Table Block Features.

Among other advantages, evenly spaced breakpoints can make the generated code division-free and reduce memory usage. For more information, see: