I read about an interesting method for decimal/binary conversion in chapter two of Gerald R. Rising’s book “Inside Your Calculator”. Unlike the standard string-oriented conversion algorithms, the algorithms in his book perform arithmetic on an encoding I’ve dubbed “deci-binary”. Using this encoding, binary numbers are input and output as decimal strings consisting of only ones and zeros, exploiting built-in language facilities for decimal input and output. I will demonstrate this conversion process with Java code I have written.
Google Reader is going away tomorrow, July 1, 2013. That means the majority of my RSS subscribers will have to find another RSS reader. I wish I could recommend one, but I am still trying to find a comparable replacement myself.
In the meantime, you could subscribe to my blog and get new articles by email.
Paul Bristow, a Boost.Math library author and reader of my blog, recently alerted me to a problem he discovered many years ago in Visual C++: some double-precision floating-point values fail to round-trip through a stringstream as a 17-digit decimal string. Interestingly, the 17-digit strings that C++ generates are not the problem; they are correctly rounded. The problem is that the conversion of those strings to floating-point is sometimes incorrect, off by one binary ULP.
I’ve previously discovered that Visual Studio makes incorrect decimal to floating-point conversions, and that Microsoft is OK with it — at least based on their response to my now deleted bug report. But incorrect decimal to floating-point conversions in this context seems like a problem that needs fixing. When you serialize a double to a 17-digit decimal string, shouldn’t you get the same double back later? Apparently Microsoft doesn’t think so, because Paul’s bug report has also been deleted.
The exact decimal equivalent of an arbitrary double-precision binary floating-point number is typically an unwieldy looking number, like this one:
In general, when you print a floating-point number, you don’t want to see all its digits; most of them are “garbage” in a sense anyhow. But how many digits do you need? You’d like a short string, yet you’d want it long enough so that it identifies the original floating-point number. A well-known result in computer science is that you need 17 significant decimal digits to identify an arbitrary double-precision floating-point number. If you were to round the exact decimal value of any floating-point number to 17 significant digits, you’d have a number that, when converted back to floating-point, gives you the original floating-point number; that is, a number that round-trips. For our example, that number is 0.10000000000000001.
But 17 digits is the worst case, which means that fewer digits — even as few as one — could work in many cases. The number required depends on the specific floating-point number. For our example, the short string 0.1 does the trick. This means that 0.1000000000000000055511151231257827021181583404541015625 and 0.10000000000000001 and 0.1 are the same, at least as far as their floating-point representations are concerned.
This is a companion article to “Number of Bits in a Decimal Integer”
If you have an integer expressed in decimal and want to know how many bits are required to express it in binary, you can perform a simple calculation. If you want to know how many bits are required to express a d-digit decimal integer in binary, you can perform other simple calculations for that.
What if you want to go in the opposite direction, that is, from binary to decimal? There are similar calculations for determining the number of decimal digits required for a specific binary integer or for a b-bit binary integer. I will show you these calculations, which are essentially the inverses of their decimal to binary counterparts.
I showed how the positive powers of ten and two are interleaved, and said that the interleaving of the negative powers of ten and two is its mirror image. In this article, I will show you why, and prove that the same properties hold.
To truly understand decimal to binary and binary to decimal conversion, you should know how the powers of ten and the powers of two relate. In particular, you should know how they are interleaved. The interleaving explains why, for example, the number of bits required to represent an n-digit decimal integer varies. Consequently, it also explains why 9,007,199,254,740,991 (253 – 1) is representable in binary floating-point, and why 9,007,199,254,740,993 (253 + 1) is not.
In this article, I will discuss the interleaving of the positive powers of ten and two, and prove some properties of it. (The interleaving of the negative powers is the mirror image of the positive powers, centered around 100 = 20 = 1.)
Excluding 0 and 1, it takes more digits to express an integer in binary than in decimal. How many more? The commonly given answer is log2(10) ≈ 3.32 times as many. But this is misleading; the ratio actually depends on the specific integer. So where does ‘log2(10) bits per digit’ come from? It’s a theoretical limit, a value that’s approached only as integers grow large. I’ll show you how to derive it.