What is the most effective way for float and double comparison?
What is the most effective way for float and double comparison?
What is the most effective way for float and double comparison?
There are basically two major pitfalls people stumble in with floating-point numbers. The problem of scale. Each FP number has an exponent which determines the overall “scale” of the number so you can represent either really small values or really larges ones, though the number of digits you can devote for that is limited. Adding … Read more
The problem you’re having relates to how floating-point numbers are represented on a computer. A more detailed discussion of floating-point representations appears towards the end of my answer (The “Floating-point representation” section). The TL;DR version: because computers have finite amounts of memory, numbers can only be represented with finite precision. Thus, the accuracy of floating-point … Read more
Because floats and doubles cannot accurately represent the base 10 multiples that we use for money. This issue isn’t just for Java, it’s for any programming language that uses base 2 floating-point types. In base 10, you can write 10.25 as 1025 * 10-2 (an integer times a power of 10). IEEE-754 floating-point numbers are … Read more
In most programming languages, floating point numbers are represented a lot like scientific notation: with an exponent and a mantissa (also called the significand). A very simple number, say 9.2, is actually this fraction: 5179139571476070 * 2 -49 Where the exponent is -49 and the mantissa is 5179139571476070. The reason it is impossible to represent … Read more
General (language agnostic) reason Since not all numbers can be represented exactly in IEEE floating point arithmetic (the standard that almost all computers use to represent decimal numbers and do math with them), you will not always get what you expected. This is especially true because some values which are simple, finite decimals (such as … Read more
Because Floating Point Math is not Associative. The way you group the operands in floating point multiplication has an effect on the numerical accuracy of the answer. As a result, most compilers are very conservative about reordering floating point calculations unless they can be sure that the answer will stay the same, or unless you … Read more
float and double are floating binary point types. In other words, they represent a number like this: 10001.10010110011 The binary number and the location of the binary point are both encoded within the value. decimal is a floating decimal point type. In other words, they represent a number like this: 12345.65789 Again, the number and … Read more
>>> a = “545.2222” >>> float(a) 545.22220000000004 >>> int(float(a)) 545
Is floating point math broken?