Integers and Floating-Point Numbers in Julia programming language

Integers and Floating-Point Numbers

Integers and floating-point values are the basic building blocks of arithmetic and computation. Built-in representations of such values are called numeric primitives, while representations of integers and floating-point numbers as immediate values in code are known as numeric literals. For example, 1 is an integer literal, while 1.0 is a floating-point literal; their binary in-memory representations as objects are numeric primitives. Julia provides a broad range of primitive numeric types, and a full complement of arithmetic and bitwise operators as well as standard mathematical functions are defined over them. The following are Julia’s primitive numeric types:

• Integer types:
  • Int8 — signed 8-bit integers ranging from –27 to 27 – 1.
  • Uint8 — unsigned 8-bit integers ranging from 0 to 28 – 1.
  • Int16 — signed 16-bit integers ranging from –215 to 215 – 1.
  • Uint16 — unsigned 16-bit integers ranging from 0 to 216 – 1.
  • Int32 — signed 32-bit integers ranging from –231 to 231 – 1.
  • Uint32 — unsigned 32-bit integers ranging from 0 to 232 – 1.
  • Int64 — signed 64-bit integers ranging from –263 to 263 – 1.
  • Uint64 — unsigned 64-bit integers ranging from 0 to 264 – 1.
  • Bool — either true or false, which correspond numerically to 1 and 0.
  • Char — a 32-bit numeric type representing a Unicode character (see Strings for more details).
• Floating-point types:
  • Float32 — IEEE 754 32-bit floating-point numbers.
  • Float64 — IEEE 754 64-bit floating-point numbers.

Additionally, full support for Complex and Rational Numbers is built on top of these primitive numeric types. All numeric types interoperate naturally without explicit casting, thanks to a flexible type promotion system. Moreover, this promotion system, detailed in Conversion and Promotion, is user-extensible, so user-defined numeric types can be made to interoperate just as naturally as built-in types.

Integers

Literal integers are represented in the standard manner:

julia> 1
1

julia> 1234
1234

The default type for an integer literal depends on whether the target system has a 32-bit architecture or a 64-bit architecture:

# 32-bit system:
julia> typeof(1)
Int32

# 64-bit system:
julia> typeof(1)
Int64

The type Int is an alias for the system-native integer type:

# 32-bit system:
julia> Int
Int32

# 64-bit system:
julia> Int
Int64

Similarly, Uint is an alias for the system-native unsigned integer type:

# 32-bit system:
julia> Uint
Uint32

# 64-bit system:
julia> Uint
Uint64

Larger integer literals that cannot be represented using only 32 bits but can be represented in 64 bits always create 64-bit integers, regardless of the system type:

# 32-bit or 64-bit system:
julia> typeof(3000000000)
Int64

Unsigned integers are input and output using the 0x prefix and hexadecimal (base 16) digits 0-9a-f (you can also use A-F for input). The size of the unsigned value is determined by the number of hex digits used:

julia> 0x1
0x01

julia> typeof(ans)
Uint8

julia> 0x123
0x0123

julia> typeof(ans)
Uint16

julia> 0x1234567
0x01234567

julia> typeof(ans)
Uint32

julia> 0x123456789abcdef
0x0123456789abcdef

julia> typeof(ans)
Uint64

This behavior is based on the observation that when one uses unsigned hex literals for integer values, one typically is using them to represent a fixed numeric byte sequence, rather than just an integer value.
The minimum and maximum representable values of primitive numeric types such as integers are given by the typemin and typemax functions:

julia> (typemin(Int32), typemax(Int32))
(-2147483648,2147483647)

julia> for T = {Int8,Int16,Int32,Int64,Uint8,Uint16,Uint32,Uint64}
         println("$(lpad(T,6)): [$(typemin(T)),$(typemax(T))]")
       end
  Int8: [-128,127]
 Int16: [-32768,32767]
 Int32: [-2147483648,2147483647]
 Int64: [-9223372036854775808,9223372036854775807]
 Uint8: [0x00,0xff]
Uint16: [0x0000,0xffff]
Uint32: [0x00000000,0xffffffff]
Uint64: [0x0000000000000000,0xffffffffffffffff]

The values returned by typemin and typemax are always of the given argument type. The above expression uses several features we have yet to introduce, including for loops, Strings, and Interpolation, but should be easy enough to understand for people with some programming experience.

Floating-Point Numbers

Literal floating-point numbers are represented in the standard formats:

julia> 1.0
1.0

julia> 1.
1.0

julia> 0.5
0.5

julia> .5
0.5

julia> -1.23
-1.23

julia> 1e10
1e+10

julia> 2.5e-4
0.00025

The above results are all Float64 values. There is no literal format for Float32, but you can convert values to Float32 easily:

julia> float32(-1.5)
-1.5

julia> typeof(ans)
Float32

There are three specified standard floating-point values that do not correspond to a point on the real number line:

• Inf — positive infinity — a value larger than all finite floating-point values
• -Inf — negative infinity — a value smaller than all finite floating-point values
• NaN — not a number — a value incomparable to all floating-point values (including itself).

For further discussion of how these non-finite floating-point values are ordered with respect to each other and other floats, see Numeric Comparisons. By the IEEE 754 standard, these floating-point values are the results of certain arithmetic operations:

julia> 1/0
Inf

julia> -5/0
-Inf

julia> 0.000001/0
Inf

julia> 0/0
NaN

julia> 500 + Inf
Inf

julia> 500 - Inf
-Inf

julia> Inf + Inf
Inf

julia> Inf - Inf
NaN

julia> Inf/Inf
NaN

The typemin and typemax functions also apply to floating-point types:

julia> (typemin(Float32),typemax(Float32))
(-Inf,Inf)

julia> (typemin(Float64),typemax(Float64))
(-Inf,Inf)

Note that Float32 values NaN, Inf and -Inf are shown identically to their Float64 counterparts.
Floating-point types also support the eps function, which gives the distance between 1.0 and the next largest representable floating-point value:

julia> eps(Float32)
1.192092896e-07

julia> eps(Float64)
2.22044604925031308e-16

These values are 2^-23 and 2^-52 as Float32 and Float64 values, respectively. The eps function can also take a floating-point value as an argument, and gives the absolute difference between that value and the next representable floating point value. That is, eps(x) yields a value of the same type as x such that x + eps(x) is the next representable floating-point value larger than x:

julia> eps(1.0)
2.22044604925031308e-16

julia> eps(1000.)
1.13686837721616030e-13

julia> eps(1e-27)
1.79366203433576585e-43

julia> eps(0.0)
4.94065645841246544e-324

As you can see, the distance to the next largest representable floating-point value is smaller for smaller values and larger for larger values. In other words, the representable floating-point numbers are densest in the real number line near zero, and grow sparser exponentially as one moves farther away from zero. By definition, eps(1.0) is the same as eps(Float64) since 1.0 is a 64-bit floating-point value.

Background and References

For a brief but lucid presentation of how floating-point numbers are represented, see John D. Cook’s article on the subject as well as his introduction to some of the issues arising from how this representation differs in behavior from the idealized abstraction of real numbers. For an excellent, in-depth discussion of floating-point numbers and issues of numerical accuracy encountered when computing with them, see David Goldberg’s paper What Every Computer Scientist Should Know About Floating-Point Arithmetic. For even more extensive documentation of the history of, rationale for, and issues with floating-point numbers, as well as discussion of many other topics in numerical computing, see the collected writings of William Kahan, commonly known as the “Father of Floating-Point”. Of particular interest may be An Interview with the Old Man of Floating-Point.

Numeric Literal Coefficients

To make common numeric formulas and expressions clearer, Julia allows variables to be immediately preceded by a numeric literal, implying multiplication. This makes writing polynomial expressions much cleaner:

julia> x = 3
3

julia> 2x^2 - 3x + 1
10

julia> 1.5x^2 - .5x + 1
13.0

You can also use numeric literals as coefficients to parenthesized expressions:

julia> 2(x-1)^2 - 3(x-1) + 1
3

Additionally, parenthesized expressions can be used as coefficients to variables, implying multiplication of the expression by the variable:

julia> (x-1)x
6

Neither juxtaposition of two parenthesized expressions, nor placing a variable before a parenthesized expression, however, can be used to imply multiplication:

julia> (x-1)(x+1)
type error: apply: expected Function, got Int64

julia> x(x+1)
type error: apply: expected Function, got Int64

Both of these expressions are interpreted as function application: any expression that is not a numeric literal, when immediately followed by a parenthetical, is interpreted as a function applied to the values in parentheses (see Functions for more about functions). Thus, in both of these cases, an error occurs since the left-hand value is not a function.
The above syntactic enhancements significantly reduce the visual noise incurred when writing common mathematical formulae. Note that no whitespace may come between a numeric literal coefficient and the identifier or parenthesized expression which it multiplies.

Syntax Conflicts

Juxtaposed literal coefficient syntax conflicts with two numeric literal syntaxes: hexadecimal integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

• The hexadecimal integer literal expression 0xff could be interpreted as the numeric literal 0 multiplied by the variable xff.
• The floating-point literal expression 1e10 could be interpreted as the numeric literal 1 multiplied by the variable e10, and similarly with the equivalent E form.

In both cases, we resolve the ambiguity in favor of interpretation as a numeric literals:

• Expressions starting with 0x are always hexadecimal literals.
• Expressions starting with a numeric literal followed by e or E are always floating-point literals.