## Numbers Greater Than DBL_MAX Should Convert To DBL_MAX When Rounding Toward Zero

I was testing David Gay’s most recent fixes to strtod() with different rounding modes and discovered that Apple Clang C++ (Xcode) and Microsoft Visual Studio C++ produce incorrect results for round towards zero and round down modes: their strtod()s convert numbers greater than DBL_MAX to infinity, not DBL_MAX. At first I thought Gay’s strtod() was wrong, but Dave pointed out that the IEEE 754 spec requires such conversions to be monotonic.

## Incorrect Hexadecimal to Floating-Point Conversions in Visual C++

Martin Brown, through a referral on his Stack Overflow question, contacted me about incorrect hexadecimal to floating-point conversions he found in Visual C++, specifically conversions using strtod() at the normal/subnormal double-precision floating-point boundary. I confirmed his examples, and also found an existing problem report for the issue. It is not your typical “off by one ULP due to rounding” conversion error; it is a conversion returning 0 for a non-zero input or returning numbers with exponents off by binary orders of magnitude.

## Jetpack Compose Byte Converter App: 2022 Version

I wrote a simple byte to decimal converter app less than two months into starting to learn Jetpack Compose. Now that I have more experience with Compose — in developing a real app and by participating on the #compose channel on Slack (login required) — I wanted to update this demo app to reflect my current understanding of best practices.

## Anomalies In IntelliJ Kotlin Floating-Point Literal Inspection

IntelliJ IDEA has a code inspection for Kotlin that will warn you if a decimal floating-point literal exceeds the precision of its type (Float or Double). It will suggest an equivalent literal (one that maps to the same binary floating-point number) that has fewer digits, or has the same number of digits but is closer to the floating-point number.

For Doubles for example, every literal over 17-digits should be flagged, since it never takes more than 17 digits to specify any double-precision binary floating-point value. Literals with 16 or 17 digits should be flagged if there is a replacement that is shorter or closer. And no literal with 15 digits or fewer should ever be flagged, since doubles have of 15-digits of precision.

But IntelliJ doesn’t always adhere to that, like when it suggests an 18-digit replacement for a 13-digit literal!

## A Simple Binary To Decimal Converter App In Jetpack Compose

I’ve been learning Jetpack Compose and Kotlin (and Android for that matter) so I decided to create a simple binary conversion app to demonstrate how easy it is to create (at least basic) UI in Compose.

(This app has been updated; see Jetpack Compose Byte Converter App: 2022 Version.)

## Direct Generation of Double Rounding Error Conversions in Kotlin

For my recent search for short examples of double rounding errors in decimal to double to float conversions I wrote a Kotlin program to generate and test random decimal strings. While this was sufficient to find examples, I realized I could do a more direct search by generating only decimal strings with the underlying double rounding error bit patterns. I’ll show you the Java BigDecimal based Kotlin program I wrote for this purpose.

## Double Rounding Errors in Decimal to Double to Float Conversions

In my previous exploration of double rounding errors in decimal to float conversions I showed two decimal numbers that experienced a double rounding error when converted to float (single-precision) through an intermediate double (double-precision). I generated the examples indirectly by setting bit combinations that forced the error, using their corresponding exact decimal representations. As a result, the decimal numbers were long (55 digits each). Mark Dickinson derived a much shorter 17 digit example, but I hadn’t contemplated how to generate even shorter numbers — or whether they existed at all — until Per Vognsen wrote me recently to ask.

The easiest way for me to approach Per’s question was to search for examples, rather than try to find a way to construct them. As such, I wrote a simple Kotlin1 program to generate decimal strings and check them. I tested all float-range (including subnormal) decimal numbers of 9 digits or fewer, and tens of billions of random 10 to 17 digit float-range (normal only) numbers. I found example 7 to 17 digit numbers that, when converted to float through a double, suffer a double rounding error.

## Maximum Number of Decimal Digits In Binary Floating-Point Numbers

I’ve written about the formulas used to compute the number of decimal digits in a binary integer and the number of decimal digits in a binary fraction. In this article, I’ll use those formulas to determine the maximum number of digits required by the double-precision (double), single-precision (float), and quadruple-precision (quad) IEEE binary floating-point formats.

The maximum digit counts are useful if you want to print the full decimal value of a floating-point number (worst case format specifier and buffer size) or if you are writing or trying to understand a decimal to floating-point conversion routine (worst case number of input digits that must be converted).

## The Safe Range For PHP’s base_convert()

PHP’s base_convert() is a useful function that converts integers between any pair of bases, 2 through 36. However, you might hesitate to use it after reading this vague and mysterious warning in its documentation:

base_convert() may lose precision on large numbers due to properties related to the internal “double” or “float” type used.

The truth is that it works perfectly for integers up to a certain maximum — you just have to know what that is. I will show you this maximum value in each of the 35 bases, and how to check if the values you are using are within this limit.