Last Updated : 05 Jul, 2024
Compacting many infinite real numbers into a finite number of bits requires an approximate representation. Most programs store the result of integer computations at 32 or 64 bits max. Given any fixed number of bits, most calculations with real numbers will produce quantities that cannot be exactly represented using that many bits. Therefore the result of a floating-point calculation must often be rounded to fit back into its finite representation. This rounding error is a characteristic feature of floating-point computation. Therefore, while handling calculations in floating point numbers, (especially if calculations are in terms of money), we need to take care of round-off errors in a programming language. Let's see an example:
Java
public class Main {
public static void main(String[] args)
{
double a = 0.7;
double b = 0.9;
double x = a + 0.1;
double y = b - 0.1;
System.out.println("x = " + x);
System.out.println("y = " + y );
System.out.println(x == y);
}
}
Output:
x = 0.7999999999999999
y = 0.8
false
Here, the answer is not what we expected reason being the rounding off done by java compiler.
Reason Behind Round Off ErrorFloat and Double data types implement IEEE floating point 754 specification. This means that numbers are represented in a form like:
SIGN FRACTION * 2 ^ EXP
0.15625 = (0.00101)2, which in floating-point format is represented as: 1.01 * 2^-3
Not all fractions can be represented exactly as a fraction of a power of two. As a simple example, 0.1 = (0.000110011001100110011001100110011001100110011001100110011001… )2 and thus cannot be stored inside a floating-point variable.
Another Example:
java
public class Main {
public static void main(String[] args)
{
double a = 0.7;
double b = 0.9;
double x = a + 0.1;
double y = b - 0.1;
System.out.println("x = " + x);
System.out.println("y = " + y );
System.out.println(x == y);
}
}
Output:
x = 0.7999999999999999
y = 0.8
false
Another example:
Java
public class Main {
public static void main(String args[])
{
double a = 1.0;
double b = 0.10;
double x = 9 * b;
a = a - (x);
// Value of a is expected as 0.1
System.out.println("a = " + a);
}
}
Output:
a = 0.09999999999999998How to Rectify Round Off Errors?
import java.math.BigDecimal;
import java.math.RoundingMode;
public class Main {
public static void main(String args[]) {
BigDecimal a = new BigDecimal("1.0");
BigDecimal b = new BigDecimal("0.10");
BigDecimal x = b.multiply(new BigDecimal("9"));
a = a.subtract(x);
// Rounding to 1 decimal place
a = a.setScale(1, RoundingMode.HALF_UP);
System.out.println("a = " + a);
}
}
Output:
0.1
Here, a = a.setScale(1, RoundingMode.HALF_UP);
Rounds a
to 1 decimal place using HALF_UP rounding mode. So, using BigDecimal provides more precise control over the arithmetic and rounding operations, which can be particularly useful for financial calculations or other cases where precision is crucial.
Math.round rounds the value to the nearest integer. As 0.10 is closer to 0 than to 1, it gets rounded to 0. After the rounding and division by 1.0, the result is 0.0. So you can notice the difference between the outputs with BigDecimal class and Maths.round function.
Java
public class Main {
public static void main(String args[])
{
double a = 1.0;
double b = 0.10;
double x = 9 * b;
a = a - (x);
/* We use Math.round() function to round the answer to
closest long, then we multiply and divide by 1.0 to
to set the decimal places to 1 place (this can be done
according to the requirements.*/
System.out.println("a = " + Math.round(a*1.0)/1.0);
}
}
Output:
0.0
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