## Differences Between Generated Code and MATLAB Code

To convert MATLAB^{®} code to efficient C/C++ code, the code generator introduces optimizations
that intentionally cause the generated code to behave differently, and sometimes produce
different results, than the original source code.

Here are some of the differences:

Output Argument Validation of Conditionally-Assigned Outputs

Size of Empty Array That Results from Deleting Elements of an Array

Growing Variable-Size Column Cell Array That is Initialized as Scalar at Run Time

Binary Element-Wise Operations with Single and Double Operands

MATLAB Classes in Nested Property Assignments That Have Set Methods

Converting Strings with Consecutive Unary Operators to double

These differences are applicable for:

MEX and standalone C/C++ code generation by using the

`codegen`

command or the MATLAB Coder™ app.Fixed-point code acceleration by generating MEX using the

`fiaccel`

(Fixed-Point Designer) command.MATLAB Function (Simulink) block simulation using Simulink

^{®}.

When you run your generated `fiaccel`

MEX, C/C++ MEX or standalone
C/C++ code, run-time error checks can detect some of these differences. By default,
run-time error checks are enabled for MEX code and disabled for standalone C/C++ code.
To help you identify and address differences before you deploy code, the code generator
reports a subset of the differences as potential differences.

### Functions that have Multiple Possible Outputs

Certain mathematical operations, such as singular value decomposition and eigenvalue decomposition of a matrix, can have multiple answers. Two different algorithms implementing such an operation can return different outputs for identical input values. Two different implementations of the same algorithm can also exhibit the same behavior.

For such mathematical operations, the corresponding functions in the generated
code and MATLAB might return different outputs for identical input values. To see if a
function has this behavior, in the corresponding function reference page, see the
**C/C++ Code Generation** section under **Extended
Capabilities**. Examples of such functions include `svd`

and `eig`

.

### Passing Input Argument Name at Run Time

Suppose that `foo`

is a function that uses name-value argument
validation. When you call `foo`

from another function
`bar`

, the code generator must be able to determine the names
that you provide to `foo`

at compile time.

If the argument names are passed at run time, code generation fails in most situations. See Names Must Be Compile-Time Constants.

In certain situations, the code generator assigns the name that you passed to an optional positional or repeating input argument. In such situations, code generation succeeds with a warning and the generated code might produce results that are different from MATLAB execution. For example, consider this function:

function out = myNamedArg_warns(a,b) out = local(a,b); end function out = local(varargin,args) arguments (Repeating) varargin end arguments args.x args.y end if isfield(args,'x') && isfield(args,'y') out = args.x / args.y; elseif isfield(args,'x') out = args.x; else out = varargin{1}; end end

#### Behavior of MATLAB Execution

If you call `myNamedArg_warns`

with `'x'`

as
the first input argument, MATLAB matches it against the first name-value argument of the function
`local`

.

`myNamedArg_warns('x',5)`

ans = 5

By contrast, if you call `myNamedArg_warns`

with
`'z'`

as the first input argument (that does not match with
either name-value argument of `local`

), MATLAB assigns the inputs into elements of
`varargin`

.

```
myNamedArg_warns('z',5)
```

ans = 'z'

#### Behavior of Generated Code

Attempt to generate a MEX by running the `codegen`

command.
Specify the type of the first argument to be a character scalar and the second
argument to be a double scalar. Code generation succeeds with a warning.

codegen myNamedArg_warns -args {'x',2}

Warning: This argument is not constant, and therefore does not match against a name-value argument inside 'myNamedArg_warns/local' during code generation. Code generation might fail or produce results that do not agree with MATLAB if a name passed at a call site is not known during code generation. Warning in ==> myNamedArg_warns Line: 2 Column: 13 Code generation successful (with warnings): View report

Irrespective of whether you pass `'x'`

or
`'z'`

as the first input argument, the generated MEX
assigns it to the first cell of `varargin`

.

`myNamedArg_warns_mex('x',5)`

ans = 'x'

`myNamedArg_warns_mex('z',5)`

ans = 'z'

**Workaround. **To enable the code generator to match the first input against the
name-value arguments of the function `local`

, declare the
first input to be a compile-time constant with value `'x'`

.
You can do this by using the `coder.Constant`

function with
the `-args`

option of the `codegen`

command.

codegen myNamedArg_warns -args {coder.Constant('x'),2}

Code generation successful.

Now, the behavior of the generated MEX agrees with MATLAB, although the MEX is unable to accept
any value other than
`'x'`

for the first input.

`myNamedArg_warns_mex('x',5)`

ans = 5

`myNamedArg_warns_mex('z',5)`

Constant function parameter 'a' has a different run-time value than the compile-time value. Error in myNamedArg_warns_mex

### Empty Repeating Input Argument

In code generation, if a repeating input argument (that is declared in an
`arguments`

block) is empty at run time, the size of that argument is
`0x0`

. By contrast, in MATLAB execution, the size of an empty repeating input argument is
`1x0`

.

For example, consider this function:

function out = testVararginSize out = local; end function out = local(varargin) arguments (Repeating) varargin end out = size(varargin); end

Running `testVararginSize`

in MATLAB returns `[1 0]`

. If you generate a MEX for
`testVararginSize`

and run the generated MEX, you get
`[0 0]`

. However, iterating over elements of
`varargin`

by using `length(varargin)`

or
`numel(varargin)`

produces the same behavior across MATLAB and code generation.

### Output Argument Validation of Conditionally-Assigned Outputs

The code generator validates an output argument if the argument is assigned a type during code generation. By contrast, MATLAB execution validates an output argument if the argument is assigned a value when the MATLAB function returns.

In most situations, this underlying behavioral difference does not cause your generated code to behave differently than MATLAB. Here is an example function for which you do see this difference:

function outerFunc(in) innerFunc(in); end function out = innerFunc(inputVal) arguments (Output) out {mustBePositive} end if inputVal out = inputVal; end end

In MATLAB, the execution of `func`

succeeds for
all double inputs. If the input is positive,
`out`

is assigned this positive value and the validator
`mustBePositive`

runs without assertion. If the input is
negative or zero, `out`

is not assigned and is not
validated.

Attempt to generate code for `func`

. Specify the input type to be
a double scalar.

codegen outerFunc -args 0

Variable 'out' is not fully defined on some execution paths. Error in ==> outerFunc Line: 7 Column: 5 Code generation failed: View Error Report

Because the variable `out`

is assigned a double scalar value on
one execution path, code generation assigns a double scalar type to
`out`

at compile time. The code generator then attempts to
perform validation on `out`

and discovers that it is not fully
defined if the `if`

condition fails.

### Writing to `ans`

Variable

When you run MATLAB code that returns an output without specifying an output argument,
MATLAB implicitly writes the output to the `ans`

variable. If the variable
`ans`

already exists in the workspace, MATLAB updates its value to the output returned.

The code generated from such MATLAB code does not implicitly write the output to an `ans`

variable.

For example, define the MATLAB function `foo`

that explicitly creates an
`ans`

variable in the first line. The function then implicitly
updates the value of `ans`

when the second line executes.

function foo %#codegen ans = 1; 2; disp(ans); end

Run `foo`

at the command line. The final value of
`ans`

, which is `2`

, is displayed at the
command line.

foo

2

Generate a MEX function from `foo`

.

`codegen foo`

Run the generated MEX function `foo_mex`

. This function
explicitly creates the `ans`

variable and assigns the value
`1`

to it. But `foo_mex`

does not implicitly
update the value of `ans`

to `2`

.

foo_mex

1

### Logical Short-Circuiting

Suppose that your MATLAB code has the logical operators `&`

and `|`

placed inside square brackets
(`[`

and `]`

). For such code patterns, the
generated code does not employ short-circuiting behavior for these logical
operators, but some MATLAB execution employs short-circuiting behavior. See Tips and Tips.

For example, define the MATLAB function `foo`

that uses the `&`

operator inside square brackets in the conditional expression of an
`if...end`

block.

function foo if [returnsFalse() & hasSideEffects()] end end function out = returnsFalse out = false; end function out = hasSideEffects out = true; disp('This is my string'); end

The first argument of the `&`

operator is always
`false`

and determines the value of the conditional expression. So,
in MATLAB execution, short-circuiting is employed and the second argument is not
evaluated. So, `foo`

does not call the
`hasSideEffects`

function during execution and does not display
anything at the command line.

Generate a MEX function for `foo`

. Call the generated MEX
function `foo_mex`

.

foo_mex

This is my string

In the generated code, short-circuiting is not employed. So, the
`hasSideEffects`

function is called and the string is displayed
at the command line.

### Loop Index Overflow

Suppose that a `for`

-loop end value is equal to or close to
the maximum or minimum value for the loop index data type. In the generated code, the last
increment or decrement of the loop index might cause the index variable to overflow. The
index overflow might result in an infinite loop.

When memory integrity checks are enabled, if the code generator detects that the loop index might overflow, it reports an error. The software error checking is conservative. It might incorrectly report a loop index overflow. By default, memory-integrity checks are enabled for MEX code and disabled for standalone C/C++ code. See Check for Issues in MATLAB Code Using MEX Functions and Generate Standalone C/C++ Code That Detects and Reports Run-Time Errors.

To avoid a loop index overflow, use the workarounds in this table.

Loop Conditions Causing the Potential Overflow | Workaround |
---|---|

The loop index increments by 1. The end value equals the maximum value of the integer type.
| If the loop does not have to cover the full range of the integer type, rewrite the loop so that the end value is not equal to the maximum value of the integer type. For example, replace: N=intmax('int16') for k=N-10:N for k=1:10 |

The loop index decrements by 1. The end value equals the minimum value of the integer type.
| If the loop does not have to cover the full range of the integer type, rewrite the loop so that the end value is not equal to the minimum value of the integer type. For example, replace: N=intmin('int32') for k=N+10:-1:N for k=10:-1:1 |

The loop index increments or decrements by 1. The start value equals the minimum or maximum value of the integer type. The end value equals the maximum or minimum value of the integer type.
| If the loop must cover the full range of the integer type, cast the type of the loop start, step, and end values to a bigger integer or to double. For example, rewrite: M= intmin('int16'); N= intmax('int16'); for k=M:N % Loop body end M= intmin('int16'); N= intmax('int16'); for k=int32(M):int32(N) % Loop body end |

The loop index increments or decrements by a value not equal to 1. On the last loop iteration, the loop index is not equal to the end value.
| Rewrite the loop so that the loop index in the last loop iteration is equal to the end value. |

### Indexing `for`

Loops by Using Single Precision Operands

Suppose in your MATLAB code, you are indexing a `for`

loop that has a colon
operator, where at least one of the colon operands is a single type operand and the
number of iterations is greater than ```
flintmax('single') =
16777216
```

. When all these conditions
are true, code generation might generate run-time or compile-time errors because the
generated code calculates different values for the loop index variable than the
values that MATLAB calculates.

For example, consider this MATLAB code:

function j = singlePIndex n = flintmax('single') + 2; j = single(0); for i = single(1):single(n) j = i; end end

This code snippet executes in MATLAB, but it causes a compile-time or run-time error because the value of
the loop index variable, `i`

, is calculated differently in the
generated code. The code generator displays a compile-time or run-time error and
stops code generation or execution to prevent this discrepancy.

To avoid this discrepancy, replace the single type operands with double type or integer type operands.

For more information on run-time errors, see Generate Standalone C/C++ Code That Detects and Reports Run-Time Errors.

### Index of an Unentered `for`

Loop

In your MATLAB code and generated code, after a `for`

loop execution
is complete, the value of the index variable is equal to its value during the final
iteration of the `for`

loop.

In MATLAB, if the loop does not execute, the value of the index variable is stored as [ ] (empty matrix). In generated code, if the loop does not execute, the value of the index variable is different than the MATLAB index variable.

If you provide the

`for`

loop start and end variables at run time, the value of the index variable is equal to the start of the range. For example, consider this MATLAB code:function out = indexTest(a,b) for i = a:b end out = i; end

Suppose that

`a`

and`b`

are passed as`1`

and`-1`

. The`for`

loop does not execute. In MATLAB,`out`

is assigned [ ]. In the generated code,`out`

is assigned the value of`a`

, which is`1`

.If you provide the

`for`

loop start and end values before compile time, the value of the index variable is assigned [ ] in both MATLAB and the generated code. Consider this MATLAB code:function out = indexTest for i = 1:-1 end out = i; end

In both MATLAB and the generated code,

`out`

is assigned [ ].

### Character Size

MATLAB supports 16-bit characters, but the generated code represents characters in 8 bits, the standard size for most embedded languages like C. See Encoding of Characters in Code Generation.

### Order of Evaluation in Expressions

Generated code does not enforce the order of evaluation in expressions. For most expressions, the order of evaluation is not significant. For expressions that have side effects, the generated code might produce the side effects in a different order from the original MATLAB code. Expressions that produce side effects include those that:

Modify persistent or global variables

Display data to the screen

Write data to files

Modify the properties of handle class objects

In addition, the generated code does not enforce order of evaluation of logical operators that do not short circuit.

For more predictable results, it is good coding practice to split expressions that depend on the order of evaluation into multiple statements.

Rewrite

A = f1() + f2();

as

A = f1(); A = A + f2();

so that the generated code calls

`f1`

before`f2`

.Assign the outputs of a multi-output function call to variables that do not depend on one another. For example, rewrite

[y, y.f, y.g] = foo;

as

[y, a, b] = foo; y.f = a; y.g = b;

When you access the contents of multiple cells of a cell array, assign the results to variables that do not depend on one another. For example, rewrite

[y, y.f, y.g] = z{:};

as

[y, a, b] = z{:}; y.f = a; y.g = b;

### Name Resolution While Constructing Function Handles

MATLAB and code generation follow different precedence rules for resolving
names that follow the symbol `@`

. These rules do not apply to
anonymous functions. The precedence rules are summarized in this table.

Expression | Precedence Order in MATLAB | Precedence Order in Code Generation |
---|---|---|

An expression that does not contain periods, for example
`@x` | Nested function, local function, private function, path function | Local variable, nested function, local function, private function, path function |

An expression that contains exactly one period, for example
`@x.y` | Local variable, path function | Local variable, path function (Same as MATLAB) |

An expression that contains more than one period, for example
`@x.y.z` | Path function | Local variable, path function |

If `x`

is a local variable that is itself a function handle,
generated code and MATLAB interpret the expression `@x`

differently:

MATLAB produces an error.

Generated code interprets

`@x`

as the function handle of`x`

itself.

Here is an example that shows this difference in behavior for an expression that contains two periods.

Suppose that your current working folder contains a MATLAB namespace `x`

, which contains another namespace
`y`

, which contains the function `z`

. The
current working folder also contains the entry-point function `foo`

for which you want to generate code.

This is the definition for the file `foo`

:

function out = foo x.y.z = @()'x.y.z is an anonymous function'; out = g(x); end function out = g(x) f = @x.y.z; out = f(); end

This is the definition for function `z`

:

function out = z out = 'x.y.z is a namespace function'; end

Generate a MEX function for `foo`

. Separately call both the
generated MEX function `foo_mex`

and the MATLAB function `foo`

.

```
codegen foo
foo_mex
foo
```

ans = 'x.y.z is an anonymous function' ans = 'x.y.z is a namespace function'

The generated code produces the first output. MATLAB produces the second output. Code generation resolves
`@x.y.z`

to the local variable `x`

that is
defined in `foo`

. MATLAB resolves `@x.y.z`

to `z`

, which is
within the namespace `x.y`

.

### Termination Behavior

Generated code does not match the termination behavior of MATLAB source code. For example, if infinite loops do not have side effects, optimizations remove them from generated code. As a result, the generated code can possibly terminate even though the corresponding MATLAB code does not.

### Size of Variable-Size N-D Arrays

For variable-size N-D arrays, the `size`

function might return a
different result in generated code than in MATLAB source code. The `size`

function sometimes returns
trailing ones (singleton dimensions) in generated code, but
always drops trailing ones in MATLAB. For example, for an N-D array `X`

with
dimensions `[4 2 1 1]`

, `size(X)`

might return
`[4 2 1 1]`

in generated code, but
always returns `[4 2]`

in
MATLAB. See Incompatibility with MATLAB in Determining Size of Variable-Size N-D Arrays.

### Size of Empty Arrays

The size of an empty array in generated code might be different from its size in MATLAB source code. See Incompatibility with MATLAB in Determining Size of Empty Arrays.

### Size of Empty Array That Results from Deleting Elements of an Array

Deleting all elements of an array results in an empty array. The size of this empty array in generated code might differ from its size in MATLAB source code.

Case | Example Code | Size of Empty Array in MATLAB | Size of Empty Array in Generated Code |
---|---|---|---|

Delete all elements of an
m-by-n array by using the `colon` operator
(`:` ). |
```
coder.varsize('X',[4,4],[1,1]);
X = zeros(2);
X(:) = [];
``` | `0-by-0` | `1-by-0` |

Delete all elements of a row
vector by using the `colon` operator
(`:` ). |
```
coder.varsize('X',[1,4],[0,1]);
X = zeros(1,4);
X(:) = [];
``` | `0-by-0` | `1-by-0` |

Delete all elements of a
column vector by using the `colon` operator
(`:` ). |
```
coder.varsize('X',[4,1],[1,0]);
X = zeros(4,1);
X(:) = [];
``` | `0-by-0` | `0-by-1` |

Delete all elements of a column vector by deleting one element at a time. |
coder.varsize('X',[4,1],[1,0]); X = zeros(4,1); for i = 1:4 X(1)= []; end | `1-by-0` | `0-by-1` |

### Growing Variable-Size Column Cell Array That is Initialized as Scalar at Run Time

In MATLAB execution, if you grow a scalar cell array by using
`{end+1}`

indexing, the cell array grows along the second
dimension and produces a row cell array. For example, define the function
`growCell`

:

function z = growCell(n, m) for i = 1:m n{end+1} = m; end z = n; end

Call `growCell`

with example inputs:

growCell({2}, 3)

ans = 1×4 cell array {[2]} {[3]} {[3]} {[3]}

By contrast, in code generation, suppose that:

You specify the cell array to be of variable-size column type (for example,

`:Inf x 1`

) at compile time,*and*Initialize this cell array as a scalar at run time.

In such situations, the generated code grows the scalar cell array
along the first dimension and produces a column cell array. For example, generate
MEX code for `growCell`

. Specify the input `n`

to
be a `:Inf x 1`

cell array with double as the underlying type.
Specify the input `m`

to be of double scalar type.

codegen growCell -args {coder.typeof({0}, [Inf 1], [1 0]), 1}

Code generation successful.

Run the generated MEX with the same inputs as before.

growCell_mex({2}, 3)

ans = 4×1 cell array {[2]} {[3]} {[3]} {[3]}

### Binary Element-Wise Operations with Single and Double Operands

If your MATLAB code contains a binary element-wise operation that involves a single type operand and a double type operand, the generated code might not produce the same result as MATLAB.

For such an operation, MATLAB casts both operands to double type and performs the operation with the double types. MATLAB then casts the result to single type and returns it.

The generated code casts the double type operand to single type. It then performs the operation with the two single types and returns the result.

For example, define a MATLAB function `foo`

that calls the binary element-wise
operation `plus`

.

function out = foo(a,b) out = a + b; end

Define a variable `s1`

of single type and a variable
`d1`

of double type. Generate a MEX function for
`foo`

that accepts a single type input and a double type
input.

s1 = single(1.4e32); d1 = -5.305e+32; codegen foo -args {s1, d1}

Call both `foo`

and `foo_mex`

with inputs
`s1`

and `d1`

. Compare the two results.

ml = foo(s1,d1); mlc = foo_mex(s1,d1); ml == mlc

ans = logical 0

The output of the comparison is a logical `0`

, which indicates
that the generated code and MATLAB produces different results for these inputs.

### Floating-Point Numerical Results

The generated code might not produce the same floating-point numerical results as MATLAB in these:

When computer hardware uses extended precision registers

### NaN and Infinity

The generated code might not produce exactly the same pattern of
`NaN`

and `Inf`

values as MATLAB code when these values are mathematically meaningless. For
example, if MATLAB output contains a `NaN`

, output from the
generated code should also contain a `NaN`

, but not necessarily
in the same place.

The bit pattern for `NaN`

can differ between MATLAB code output and generated code output because the C99 language
standard that is used to generate code does not specify a unique bit pattern for
`NaN`

across all
implementations. Avoid comparing bit patterns across different implementations,
for example, between MATLAB output and SIL or PIL output.

### Negative Zero

In a floating-point type, the value `0`

has either a positive
sign or a negative sign. Arithmetically, `0`

is equal to
`-0`

, but some operations are sensitive to the sign of a 0
input. Examples include `rdivide`

, `atan2`

,
`atan2d`

, and `angle`

. Division by
`0`

produces `Inf`

, but division by
`-0`

produces `-Inf`

. Similarly,
`atan2d(0,-1)`

produces `180`

, but
`atan2d (-0,-1)`

produces `-180`

.

If the code generator detects that a floating-point variable takes only integer
values of a suitable range, then the code generator can use an integer type for the
variable in the generated code. If the code generator uses an integer type for the
variable, then the variable stores `-0`

as `+0`

because an integer type does not store a sign for the value `0`

. If
the generated code casts the variable back to a floating-point type, the sign of
`0`

is positive. Division by `0`

produces
`Inf`

, not `-Inf`

. Similarly,
`atan2d(0,-1)`

produces `180`

, not
`-180`

.

There are other contexts in which the generated code might treat
`-0`

differently than MATLAB. For example, suppose that your MATLAB code computes the minimum of two scalar doubles `x`

and `y`

by using `z = min(x,y)`

. The corresponding
line in the generated C code might be `z = fmin(x,y)`

. The function
`fmin`

is defined in the runtime math library of the C
compiler. Because the comparison operation `0.0 == -0.0`

returns
`true`

in C/C++, the compiler's implementation of
`fmin`

might return either `0.0`

or
`-0.0`

for `fmin(0.0,-0.0)`

.

### Code Generation Target

The `coder.target`

function returns different values in
MATLAB than in the generated code. The intent is to help you determine
whether your function is executing in MATLAB or has been compiled for a simulation or code generation target.
See `coder.target`

.

### MATLAB Class Property Initialization

Before code generation, at class loading time, MATLAB computes class default values. The code generator uses the values that MATLAB computes. It does not recompute default values. If the property definition uses a function call to compute the initial value, the code generator does not execute this function. If the function has side effects, such as modifying a global variable or a persistent variable, then the generated code can produce different results than MATLAB. For more information, see Defining Class Properties for Code Generation.

### MATLAB Classes in Nested Property Assignments That Have Set Methods

When you assign a value to a handle object property, which is itself a property of another object, and so on, then the generated code can call set methods for handle classes that MATLAB does not call.

For example, suppose that you define a set of variables such that
`x`

is a handle object, `pa`

is an object,
`pb`

is a handle object, and `pc`

is a
property of `pb`

. Then you make a nested property assignment, such
as:

x.pa.pb.pc = 0;

In this case, the generated code calls the set method for the object
`pb`

and the set method for `x`

. MATLAB calls only the set method for `pb`

.

### MATLAB Handle Class Destructors

The behavior of handle class destructors in the generated code can be different from the behavior in MATLAB in these situations:

The order of destruction of several independent objects might be different in MATLAB than in the generated code.

The lifetime of objects in the generated code can be different from their lifetime in MATLAB.

The generated code does not destroy partially constructed objects. If a handle object is not fully constructed at run time, the generated code produces an error message but does not call the

`delete`

method for that object. For a System object™, if there is a run-time error in`setupImpl`

, the generated code does not call`releaseImpl`

for that object.MATLAB does call the

`delete`

method to destroy a partially constructed object.

For more information, see Code Generation for Handle Class Destructors.

### Variable-Size Data

See Incompatibilities with MATLAB in Variable-Size Support for Code Generation.

### Complex Numbers

### Converting Strings with Consecutive Unary Operators to
`double`

Converting a string that contains multiple, consecutive unary operators to
`double`

can produce different results between MATLAB and the generated code. Consider this
function:

function out = foo(op) out = double(op + 1); end

For an input value `"--"`

, the function converts the string
`"--1"`

to `double`

. In MATLAB, the answer is `NaN`

. In the generated code, the
answer is `1`

.

### Display Function

Statements and expressions in MATLAB code that omit the semicolon implicitly invoke the
`display`

function. You can also explicitly invoke
`display`

as shown
here:

display(2+3);

5

The MEX code generated for MATLAB code that invokes the `display`

function preserves
calls to this function and shows the output. In standalone code generated for
targets that do not have access to MATLAB Runtime, implicit and explicit calls to `display`

are
removed. This includes calls to overridden class methods of
`display`

.

To display text in code generated for other targets, override the
`disp`

function in your MATLAB classes. For example:

%MATLAB Class classdef foo methods function obj = foo end function disp(self) disp("Overridden disp"); end end end %Entry-point Function function callDisp a = foo; disp(a); end

The generated code for the entry-point function is shown here:

/* Include Files */ #include "callDisp.h" #include <stdio.h> /* Function Definitions */ /* * Arguments : void * Return Type : void */ void callDisp(void) { printf("%s\n", "Overridden disp"); fflush(stdout); }

#### Function Handle Difference

Invoking `display`

through a function handle in MATLAB prints the name of the variable as well. For example, running this
function in MATLAB results in the following
output:

function displayDiff z = 10; f = @display; f(z) end

z = 10

However, the generated code for this snippet only outputs the value
`10`

.