Truncating is a process of shortening something by cutting it off. In the context of computer science, it is often used to refer to data or variables, such as floating point numbers and strings. The global truncation error is the accumulation of the local truncation error in all iterations, assuming a perfect knowledge of the true solution in the initial period of time. Check truncation, as used in American English, is a check clearing system that involves the elimination or elimination of physical processing of paper checks and their replacement by electronic means.
In computer applications, truncation error is the discrepancy that arises when executing a finite number of steps to approach an infinite process. A truncation symbol is added to the end of the root of a word to tell the database to search for all forms of a word. The trunc() function truncates the value after the decimal and returns only the integer part. In IT, truncation refers to “cutting something or removing parts of it to shorten it”.
To keep things simple, let's look at the algorithm for truncating positive numbers (and the most motivated among you can see if you can find an algorithm for truncating negative numbers). Truncation shortens a decimal-based value, such as a float or a double, to its int integral form with the additional precision bits after the decimal (from 2-1 in the corresponding decimal form) removed. If the step size h between two adjacent values Ik becomes smaller, the truncation error of the numerical integration rule decreases. Strings can also be truncated, which can be useful if a string exceeds the maximum character limit for a given application.
Truncation errors occur when a primitive becomes a smaller primitive and data is lost in the conversion. Narrowing is another process of shortening something, but unlike truncation, it is not limited to shortening a floating point value to an integer value. Note that the domains of the truncation function and the rounding function for any given return value are exactly offset by 0.5.The algorithm for truncating positive and negative numbers differs slightly due to the elimination of the final bits of precision by truncating the value from double to float (which has only 23 bits of precision, from 0 to 2), ordinally decreasing the precision bits in this case. A code truncation operation can take a number with several digits to the left of the decimal point and truncate that number to an integer.