# ge, >=

Determine greater than or equal to

## Description

## Examples

### Test Vector Elements

Find which vector elements are greater than or equal to a given value.

Create a numeric vector.

A = [1 12 18 7 9 11 2 15];

Test the vector for elements that are greater than or equal to `11`

.

A >= 11

`ans = `*1x8 logical array*
0 1 1 0 0 1 0 1

The result is a vector with values of logical `1`

(`true`

) where the elements of `A`

satisfy the expression.

Use the vector of logical values as an index to view the values in `A`

that are greater than or equal to `11`

.

A(A >= 11)

`ans = `*1×4*
12 18 11 15

The result is a subset of the elements in `A`

.

### Replace Elements of Matrix

Create a matrix.

A = magic(4)

`A = `*4×4*
16 2 3 13
5 11 10 8
9 7 6 12
4 14 15 1

Replace all values greater than or equal to `9`

with the value `10`

.

A(A >= 9) = 10

`A = `*4×4*
10 2 3 10
5 10 10 8
10 7 6 10
4 10 10 1

The result is a new matrix whose largest element is `10`

.

### Compare Values in Categorical Array

Create an ordinal categorical array.

A = categorical({'large' 'medium' 'small'; 'medium' ... 'small' 'large'},{'small' 'medium' 'large'},'Ordinal',1)

`A = `*2x3 categorical*
large medium small
medium small large

The array has three categories: `'small'`

, `'medium'`

, and `'large'`

.

Find all values greater than or equal to the category `'medium'`

.

`A >= 'medium'`

`ans = `*2x3 logical array*
1 1 0
1 0 1

A value of logical `1`

(`true`

) indicates a value greater than or equal to the category `'medium'`

.

Compare the rows of `A`

.

A(1,:) >= A(2,:)

`ans = `*1x3 logical array*
1 1 0

The function returns logical `1`

(`true`

) where the first row has a category value greater than or equal to the second row.

### Test Complex Numbers

Create a vector of complex numbers.

A = [1+i 2-2i 1+3i 1-2i 5-i];

Find the values that are greater than or equal to `2`

.

A(A >= 2)

`ans = `*1×2 complex*
2.0000 - 2.0000i 5.0000 - 1.0000i

`ge`

compares only the real part of the elements in `A`

.

Use `abs`

to find which elements are outside a radius of `2`

from the origin.

A(abs(A) >= 2)

`ans = `*1×4 complex*
2.0000 - 2.0000i 1.0000 + 3.0000i 1.0000 - 2.0000i 5.0000 - 1.0000i

The result has more elements since `abs`

accounts for the imaginary part of the numbers.

### Test Duration Values

Create a `duration`

array.

d = hours(21:25) + minutes(75)

`d = `*1x5 duration*
22.25 hr 23.25 hr 24.25 hr 25.25 hr 26.25 hr

Test the array for elements that are greater than or equal to one standard day.

d >= 1

`ans = `*1x5 logical array*
0 0 1 1 1

## Input Arguments

`A`

, `B`

— Operands

scalars | vectors | matrices | multidimensional arrays

Operands, specified as scalars, vectors, matrices, or multidimensional arrays. Inputs
`A`

and `B`

must either be the same size or have
sizes that are compatible (for example, `A`

is an
`M`

-by-`N`

matrix and `B`

is a
scalar or `1`

-by-`N`

row vector). For more
information, see Compatible Array Sizes for Basic Operations.

You can compare numeric inputs of any type, and the comparison does not suffer loss of precision due to type conversion.

If one input is an ordinal

`categorical`

array, the other input can be an ordinal`categorical`

array, a cell array of character vectors, or a single character vector. A single character vector expands into a cell array of character vectors of the same size as the other input. If both inputs are ordinal`categorical`

arrays, they must have the same sets of categories, including their order. See Compare Categorical Array Elements for more details.If one input is a

`datetime`

array, the other input can be a`datetime`

array, a character vector, or a cell array of character vectors.If one input is a

`duration`

array, the other input can be a`duration`

array or a numeric array. The operator treats each numeric value as a number of standard 24-hour days.If one input is a string array, the other input can be a string array, a character vector, or a cell array of character vectors. The corresponding elements of

`A`

and`B`

are compared lexicographically.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

| `logical`

| `char`

| `string`

| `categorical`

| `datetime`

| `duration`

**Complex Number Support: **Yes

## Tips

Some floating-point numbers cannot be represented exactly in binary form. This leads to small differences in results that the

`>=`

operator reflects. For more information, see Avoiding Common Problems with Floating-Point Arithmetic.

## Extended Capabilities

### Tall Arrays

Calculate with arrays that have more rows than fit in memory.

This function fully supports tall arrays. For more information, see Tall Arrays.

### C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

### GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

### HDL Code Generation

Generate Verilog and VHDL code for FPGA and ASIC designs using HDL Coder™.

### Thread-Based Environment

Run code in the background using MATLAB® `backgroundPool`

or accelerate code with Parallel Computing Toolbox™ `ThreadPool`

.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

### GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

### Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

## Version History

**Introduced before R2006a**

### R2020b: Implicit expansion change affects ordinal `categorical`

arrays, `datetime`

arrays, and `duration`

arrays

*Behavior changed in R2020b*

Starting in R2020b, `ge`

supports implicit expansion when the
arguments are ordinal `categorical`

arrays, `datetime`

arrays, or `duration`

arrays. Between R2020a and R2016b, implicit expansion
was supported only for numeric and string data types.

### R2016b: Implicit expansion change affects arguments for operators

*Behavior changed in R2016b*

Starting in R2016b with the addition of implicit expansion, some combinations of arguments for basic operations that previously returned errors now produce results. For example, you previously could not add a row and a column vector, but those operands are now valid for addition. In other words, an expression like `[1 2] + [1; 2]`

previously returned a size mismatch error, but now it executes.

If your code uses element-wise operators and relies on the errors that MATLAB^{®} previously returned for mismatched sizes, particularly within a `try`

/`catch`

block, then your code might no longer catch those errors.

For more information on the required input sizes for basic array operations, see Compatible Array Sizes for Basic Operations.

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