How Can We Overcome Accuracy Issues in Floating-Point Arithmetic?

Overcoming Accuracy Issues in Floating-Point Numbers

Floating-point numbers, a widely used data type in computing, present potential accuracy problems due to their intrinsic representation in the machine. To illustrate this issue:

In this code, str is a string representation of "4.600", and calling atof(str) converts it to a double mw. The subsequent calculations yield an unfortunate result: the floating-point representation of 4.5999999999999996 cannot precisely represent the desired value of 4.600.

Addressing the Issue

As highlighted by the solution, achieving exact equality with floating-point arithmetic is practically impossible. Instead, it's preferable to compare values within an acceptable range. In certain scenarios, it's essential to recognize that specific values cannot be accurately represented as floating-point numbers.

Recommended Resources

For further insights, consult the following references:

• [What Every Computer Scientist Should Know About Floating-Point Arithmetic](https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html)
• [Comparing floating point numbers](https://floating-point-gui.de/)

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