How to Overcome Floating Point Errors While Preserving Accuracy in Calculations?

How to Handle Floating Point Errors Without Sacrificing Accuracy

When working with floating point arithmetic, you may encounter errors due to the approximate nature of its representation. This can pose a challenge when aiming for high accuracy in your calculations.

One approach to tackling this issue is to understand the limitations of floating point representation. In Python, the binary floating-point used ("double precision") typically represents decimal values using approximations. This means that adding a small value like 0.01 is not precise and may result in unexpected errors, as in the example provided:

<code class="python">def sqrt(num):
    root = 0.0
    while root * root <p>To avoid such errors, you can utilize Python's decimal module. The Decimal type allows for accurate decimal arithmetic, ensuring that values like 0.01 are represented exactly. By modifying the sqrt function to use the Decimal type, you can eliminate rounding errors:</p>
<pre class="brush:php;toolbar:false"><code class="python">from decimal import Decimal as D

def sqrt(num):
    root = D(0)
    while root * root <p>Alternatively, if sticking to floats is preferred, you can increment your calculations using values that are precisely representable as binary floats. This involves using values in the form I/2**J, such as 0.125 (1/8) or 0.0625 (1/16).</p>
<p>Furthermore, employing Newton's method for calculating square roots can also enhance accuracy when dealing with floating point arithmetic.</p></code>

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