Incorrect Decimal to Floating-Point Conversion In SQLite

SQLite has a limited-precision floating-point to decimal conversion routine which it uses to print double-precision floating-point values retrieved from a database. As I’ve discovered, its limited-precision conversion results in decimal numbers of 15 significant digits or less that won’t round-trip. For example, if you store the number 9.944932e+31, it will print back as 9.94493200000001e+31.

SQLite also has a limited-precision decimal to floating-point conversion routine, which it uses to convert input decimal numbers to double-precision floating-point numbers for storage in a database. I’ve found that some of its conversions are incorrect — by as many as four ULPs — and that some decimal numbers fail to round-trip because of this; “garbage in, garbage out” as they say.

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Fifteen Digits Don’t Round-Trip Through SQLite Reals

I’ve discovered that decimal floating-point numbers of 15 significant digits or less don’t always round-trip through SQLite. Consider this example, executed on version 3.7.3 of the pre-compiled SQLite command shell:

sqlite> create table t1(d real);
sqlite> insert into t1 values(9.944932e+31);
sqlite> select * from t1;

SQLite represents a decimal floating-point number that has real affinity as a double-precision binary floating-point number — a double. A decimal number of 15 significant digits or less is supposed to be recoverable from its double-precision representation. In SQLite, however, this guarantee is not met; this is because its floating-point to decimal conversion routine is implemented in limited-precision floating-point arithmetic.

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Quick and Dirty Floating-Point to Decimal Conversion

In my article “Quick and Dirty Decimal to Floating-Point Conversion” I presented a small C program that uses double-precision floating-point arithmetic to convert decimal strings to binary floating-point numbers. The program converts some numbers incorrectly, despite using an algorithm that’s mathematically correct; its limited precision calculations are to blame. I dubbed the program “quick and dirty” because it’s simple, and overall converts reasonably accurately.

For this article, I took a similar approach to the conversion in the opposite direction — from binary floating-point to decimal string. I wrote a small C program that combines two mathematically correct algorithms: the classic “repeated division by ten” algorithm to convert integer values, and the classic “repeated multiplication by ten” algorithm to convert fractional values. The program uses double-precision floating-point arithmetic, so like its quick and dirty decimal to floating-point counterpart, its conversions are not always correct — though reasonably accurate. I’ll present the program and analyze some example conversions, both correct and incorrect.

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Incorrect Floating-Point to Decimal Conversions

In my article “Inconsistent Rounding of Printed Floating-Point Numbers” I showed examples of incorrect floating-point to decimal conversions I stumbled upon — in Java, Visual Basic, JavaScript, VBScript, and Calc. In this article, I’ll explore floating-point to decimal conversions more deeply, by analyzing conversions done under four C compilers: Visual C++, MinGW GCC, Digital Mars C, and Linux GCC. I found that incorrect conversions occur in three of the four environments — in all but Linux GCC. I’ll show you some examples and explain how I found them.

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Inconsistent Rounding of Printed Floating-Point Numbers

What does this C program print?

#include <stdio.h>
int main (void)
 printf ("%.1f\n",0.25);

The answer depends on which compiler you use. If you compile the program with Visual C++ and run on it on Windows, it prints 0.3; if you compile it with gcc and run it on Linux, it prints 0.2.

The compilers — actually, their run time libraries — are using different rules to break decimal rounding ties. The two-digit number 0.25, which has an exact binary floating-point representation, is equally near two one-digit decimal numbers: 0.2 and 0.3; either is an acceptable answer. Visual C++ uses the round-half-away-from-zero rule, and gcc (actually, glibc) uses the round-half-to-even rule, also known as bankers’ rounding.

This inconsistency of printed output is not limited to C — it spans many programming environments. In all, I tested fixed-format printing in nineteen environments: in thirteen of them, round-half-away-from-zero was used; in the remaining six, round-half-to-even was used. I also discovered an anomaly in some environments: numbers like 0.15 — which look like halfway cases but are actually not when viewed in binary — may be rounded incorrectly. I’ll report my results in this article.

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Hexadecimal Floating-Point Constants

Hexadecimal floating-point constants, also known as hexadecimal floating-point literals, are an alternative way to represent floating-point numbers in a computer program. A hexadecimal floating-point constant is shorthand for binary scientific notation, which is an abstract — yet direct — representation of a binary floating-point number. As such, hexadecimal floating-point constants have exact representations in binary floating-point, unlike decimal floating-point constants, which in general do not.

Hexadecimal floating-point constants are useful for two reasons: they bypass decimal to floating-point conversions, which are sometimes done incorrectly, and they bypass floating-point to decimal conversions which, even if done correctly, are often limited to a fixed number of decimal digits. In short, their advantage is that they allow for direct control of floating-point variables, letting you read and write their exact contents.

In this article, I’ll show you what hexadecimal floating-point constants look like, and how to use them in C.

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Finding Numbers That Are Palindromic In Multiple Bases

A palindromic number, or number palindrome, is a number like 74347, which is the same written forward and backward.

A number can be palindromic in any base, not just decimal. For example, 101101 is a palindrome in binary. A number can also be palindromic in more than one base, like decimal 719848917, which is 101010111010000000010111010101 in binary and 5272002725 in octal.

An efficient way to find palindromes in a single base is to generate them, iterating through each integer and constructing palindromes from them. An efficient way to find numbers that are palindromic in multiple bases is to take a palindrome in one base and test if it’s a palindrome in one or more additional bases.

In this article, I’ll show you C code I wrote that finds multi-base numeric palindromes. I used this code to generate tables of numbers that are palindromic in decimal and binary, decimal and hexadecimal, and decimal and octal. I also used this code to solve Euler problem 36, which asks for the sum of all numbers, less than one million, that are palindromic in decimal and binary.

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Exploring Binary Numbers With PARI/GP Calculator

PARI/GP is an open source computer algebra system I use frequently in my study of binary numbers. It doesn’t manipulate binary numbers directly — input, and most output, is in decimal — so I use it mainly to do the next best thing: calculate with powers of two. Calculations with powers of two are, indirectly, calculations with binary numbers.

PARI/GP is a sophisticated tool, with several components — yet it’s easy to install and use. I use its command shell in particular, the PARI/GP calculator, or gp for short. I will show you how to use simple gp commands to explore binary numbers.

PARI/GP Calculator (Example Calculations) (My Setup on Windows)
PARI/GP Calculator (Sample of Calculations Used to Explore Binary Numbers)

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Nines in Binary

I discovered a cool property of positive integers of the form 10n-1, that is, integers made up of n digits of 9s: they have binary representations that have exactly n digits of trailing 1s. For example, 9,999,999 in decimal is 100110001001011001111111 in binary.

The property is interesting in and of itself, but what is more interesting is the process I went through to discover it. It’s a small-scale example of experimental mathematics: I observed something interesting, experimented to collect more data, developed a hypothesis, and constructed a proof.

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How I Taught My Mother Binary Numbers

I introduced my mother to binary numbers a few weeks ago when I showed her my One Hundred Cheerios in Binary poster. It shows the decimal number 100 in binary — 1100100. She’s not an engineer but she’s good with numbers, so I knew she would get it — if only I could find the right way to explain it. Two days ago, I found the right way.

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Converting Floating-Point Numbers to Binary Strings in C

If you want to print a floating-point number in binary using C code, you can’t use printf() — it has no format specifier for it. That’s why I wrote a program to do it, a program I describe in this article.

(If you’re wondering why you’d want to print a floating-point number in binary, I’ll tell you that too.)

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