# c++ java - What is the difference between float and double?

Huge difference.

As the name implies, a `double`

has 2x the precision of `float`

^{[1]}. In general a `double`

has 15 decimal digits of precision, while `float`

has 7.

Here's how the number of digits are calculated:

`double`

has 52 mantissa bits + 1 hidden bit: log(2^{53})÷log(10) = 15.95 digits

`float`

has 23 mantissa bits + 1 hidden bit: log(2^{24})÷log(10) = 7.22 digits

This precision loss could lead to truncation errors much easier to float up, e.g.

```
float a = 1.f / 81;
float b = 0;
for (int i = 0; i < 729; ++ i)
b += a;
printf("%.7g\n", b); // prints 9.000023
```

while

```
double a = 1.0 / 81;
double b = 0;
for (int i = 0; i < 729; ++ i)
b += a;
printf("%.15g\n", b); // prints 8.99999999999996
```

Also, the maximum value of float is about `3e38`

, but double is about `1.7e308`

, so using `float`

can hit "infinity" (i.e. a special floating-point number) much more easily than `double`

for something simple, e.g. computing the factorial of 60.

During testing, maybe a few test cases contain these huge numbers, which may cause your programs to fail if you use floats.

Of course, sometimes, even `double`

isn't accurate enough, hence we sometimes have `long double`

^{[1]} (the above example gives 9.000000000000000066 on Mac), but all floating point types suffer from *round-off errors*, so if precision is very important (e.g. money processing) you should use `int`

or a fraction class.

Furthermore, don't use `+=`

to sum lots of floating point numbers, as the errors accumulate quickly. If you're using Python, use `fsum`

. Otherwise, try to implement the Kahan summation algorithm.

^{[1]: The C and C++ standards do not specify the representation of float, double and long double. It is possible that all three are implemented as IEEE double-precision. Nevertheless, for most architectures (gcc, MSVC; x86, x64, ARM) float is indeed a IEEE single-precision floating point number (binary32), and double is a IEEE double-precision floating point number (binary64).}

I've read about the difference between double precision and single precision. However, in most cases, `float`

and `double`

seem to be interchangeable, i.e. using one or the other does not seem to affect the results. Is this really the case? When are floats and doubles interchangeable? What are the differences between them?

Given a quadratic equation: *x*^{2} − 4.0000000 *x* + 3.9999999 = 0, the exact roots to 10 significant digits are, *r*_{1} = 2.000316228 and *r*_{2} = 1.999683772.

Using `float`

and `double`

, we can write a test program:

```
#include <stdio.h>
#include <math.h>
void dbl_solve(double a, double b, double c)
{
double d = b*b - 4.0*a*c;
double sd = sqrt(d);
double r1 = (-b + sd) / (2.0*a);
double r2 = (-b - sd) / (2.0*a);
printf("%.5f\t%.5f\n", r1, r2);
}
void flt_solve(float a, float b, float c)
{
float d = b*b - 4.0f*a*c;
float sd = sqrtf(d);
float r1 = (-b + sd) / (2.0f*a);
float r2 = (-b - sd) / (2.0f*a);
printf("%.5f\t%.5f\n", r1, r2);
}
int main(void)
{
float fa = 1.0f;
float fb = -4.0000000f;
float fc = 3.9999999f;
double da = 1.0;
double db = -4.0000000;
double dc = 3.9999999;
flt_solve(fa, fb, fc);
dbl_solve(da, db, dc);
return 0;
}
```

Running the program gives me:

```
2.00000 2.00000
2.00032 1.99968
```

Note that the numbers aren't large, but still you get cancellation effects using `float`

.

(In fact, the above is not the best way of solving quadratic equations using either single- or double-precision floating-point numbers, but the answer remains unchanged even if one uses a more stable method.)

The size of the numbers involved in the float-point calculations is not the most relevant thing. It's the calculation that is being performed that is relevant.

In essence, if you're performing a calculation and the result is an irrational number or recurring decimal, then there will be rounding errors when that number is squashed into the finite size data structure you're using. Since double is twice the size of float then the rounding error will be a lot smaller.

The tests may specifically use numbers which would cause this kind of error and therefore tested that you'd used the appropriate type in your code.

Type float, 32 bits long, has a precision of 7 digits. While it may store values with very large or very small range (+/- 3.4 * 10^38 or * 10^-38), it has only 7 significant digits.

Type double, 64 bits long, has a bigger range (*10^+/-308) and 15 digits precision.

Type long double is nominally 80 bits, though a given compiler/OS pairing may store it as 12-16 bytes for alignment purposes. The long double has an exponent that just ridiculously huge and should have 19 digits precision. Microsoft, in their infinite wisdom, limits long double to 8 bytes, the same as plain double.

Generally speaking, just use type double when you need a floating point value/variable. Literal floating point values used in expressions will be treated as doubles by default, and most of the math functions that return floating point values return doubles. You'll save yourself many headaches and typecastings if you just use double.

When using floating point numbers you cannot trust that your local tests will be exactly the same as the tests that are done on the server side. The environment and the compiler are probably different on you local system and where the final tests are run. I have seen this problem many times before in some TopCoder competitions especially if you try to compare two floating point numbers.

Unlike an `int`

(whole number), a `float`

have a decimal point, and so can a `double`

.
But the difference between the two is that a `double`

is twice as detailed as a `float`

, meaning that it can have double the amount of numbers after the decimal point.