Why Does a Floating-Point Calculation Result in 1.9999999999999996 Instead of 2.0?

Handling Accuracy Discrepancies in Floating-Point Representations

When working with floating-point numbers, precision limitations due to their internal representation can lead to unexpected results. Consider the following issue:

In a program, a variable named mw is initialized with the value 4.6, obtained by converting a string with this value to a double. However, when used in a calculation to determine the number of columns, the result comes out as 1.9999999999999996 instead of the expected 2.0.

This discrepancy arises because the internal representation of floating-point numbers is approximate. In this case, 4.6 cannot be precisely stored as a float, resulting in a slightly different value of 4.5999999999999996 being used in the calculation.

To address this issue, it is important to recognize that strict equality comparisons with floating-point numbers are unreliable. Instead, consider using a range of acceptable values to determine if a result is close enough to the expected outcome.

Furthermore, be aware that some values cannot be precisely represented as floating-point numbers. For more information, refer to comprehensive resources such as "What Every Computer Scientist Should Know About Floating-Point Arithmetic" and "Comparing Floating Point Numbers." By understanding these limitations and applying appropriate techniques, you can mitigate accuracy problems when working with floating-point numbers.

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