On this site, we occasionally have questions requiring programs to be "radiation hardened"; this means that the program has to be able to survive the deletion of one or more bytes, no matter which bytes are deleted.

As is common for tasks that frequently get set in programming challenges, it's natural to want to make a language that's particularly good at these challenges. Given that the natural way to make this is to add some metadata that makes it possible to reverse corruption, it's actually not really a language that needs designing, but an encoding; the idea is to transform each input to a sequence of bytes, in such a way that even if the sequence is slightly irradiated, it's possible to extract the original input.

The task

Write two programs or functions, E (an encoder) and D (a decoder), such that:

  • E takes two arguments, a sequence of octets (which we'll call "input" in this specification) and a nonnegative integer "radiation", and outputs a sequence of octets "encoding";
  • D takes one argument, a sequence of octets ("encdng"), and outputs a sequence of octets "reconstruction";
  • If you run both E and D (with encdng, the input to D, chosen by deleting no more than radiation elements from encoding (not necessarily contiguously)), then reconstruction will be equal to input no matter which characters were deleted to form encdng.


  • If you submit functions, you don't have to call them E and D; you can choose whatever name is most suitable for your language.
  • An "octet" is basically an integer from 0 to 255 inclusive, which you can encode as an integer, a character, or whatever's appropriate for your language.
  • E and D must be entirely deterministic (i.e. giving them the same inputs will always produce the same output, where "inputs" is defined as input and radiation for E, or encdng for D). In particular, E may not communicate information to D via a side channel.
  • The deletions are performed by deleting one element of the sequence; think of opening the seuqence in an editor, placing the cursor at an arbitrary point, and pressing Backspace. If an element appears multiple times, it's possible that only one copy of the element will be deleted (i.e. other instances of the same octet won't be affected).
  • Although the score is only calculated on the basis of fairly short input, your program must work in theory for any input and radiation. In particular, it must work no matter which octets appear in input. (Sorry, people who would like the ability to use unprintable characters that they know won't appear in the input, but I need to ensure that the input's incompressible so that the challenge is about radiation hardening rather than compression.)
  • You can submit either one file that defines two functions; two files which each define a function or which are both full programs; or three files, two of which implement D and E respectively (either via being full programs or via defining a function), and the third which is a header file or library common to both D and E. Regardless of which form of submission you use, your programming language implementation must be able to understand both programs without further arguments like file locations (or else you must pay a byte penalty for invoking your implementation in an unusual way, as per our standard rules).

Victory condition

For each length and radiation, let f(length,radiation) be the total lengths of the encodings that correspond to all input with length length, and the given radiation. (That is, f(length,radiation) = suminput has length length length(E(input,radiation)).) Then let g(length,radiation) equal f(length,radiation) ÷ 256length. In other words, g is the average length of the encoded output, for a given length of input and a given radiation hardening requirement. (In theory you could calculate this by brute force, but it would likely take implausibly long to work out your score that way. I'm expecting most submissions will be able to make a mathematical argument as to what their score is. If you're not sure, post an approximate score and you or someone else can calculate it in more depth if another entry posts a similar score.)

Your score is equal to the sum of g(length,radiation) for all radiation in the range 0 to 9 inclusive, and all length in the range 0 to 99 inclusive, plus (mostly to avoid hardcoding, or to keep the competition going if someone discovers a mathematically perfect encoding; this is likely to be a minimal factor otherwise) the total number of bytes in your submission to the challenge (plus the standard penalties for things like requiring unusual interpreter flags or specific filenames). The winner is the entry with the lowest score (tiebroken by the first entry to submit).

  • \$\begingroup\$ May the decoder know the radiation parameter as well? \$\endgroup\$
    – orlp
    Commented Feb 23, 2017 at 6:54
  • \$\begingroup\$ (or length, but I believe that knowing either should give you the other for most schemes) \$\endgroup\$
    – orlp
    Commented Feb 23, 2017 at 7:01
  • 1
    \$\begingroup\$ @orlp: No, it only has the string. In a radiation-hardening problem, the decoder (i.e. language) doesn't know the radiation rules used, so your decoder doesn't know them either; it has to deduce them from its input. \$\endgroup\$
    – user62131
    Commented Feb 23, 2017 at 12:39
  • \$\begingroup\$ Based on this computerphile video I'd make a language that takes codels in 3-byte triplets: if they don't all match, something went wrong and we have enough information to figure out what the right value is. There's probably a way to do it with fewer bits but I don't have the brain to work out how at the moment. \$\endgroup\$ Commented Jun 19, 2017 at 17:08
  • \$\begingroup\$ How do you combine the header and the programs? \$\endgroup\$ Commented Jun 19, 2017 at 19:43

3 Answers 3


CJam, score ≤ 286,516 + 54 + 36 = 286,606



Try it online!



Try it online!

Both of these take and return a list integers. The TIO links include conversion from/to strings for convenience. Note that these are incredibly inefficient for longer strings. If you want to try a few more characters, I recommend using characters with small character codes.

The basic idea for creating a radiation-hardened encoding involves two steps:

  1. Find an encoding which never contains two consecutive identical octets.
  2. Repeat each octet in the encoded string r + 1 times, where r is the radiation level.

This way, the radiation can't completely delete one run of identical characters, so that we can decode the string by taking one character from each run and then decoding step 1.

So the only interesting part is finding an encoding that never yields repeated octets. The basic idea is to use something like A043096 as a number system. That is, to encode an integer N, we simply count up in some base b, skipping all numbers with repeated octets. I believe the amount of numbers that can be represented in this way with up to d digits is the same as the amount of numbers that can be represented in bijective base b-1 (since, when you want to write such a number, you can choose between b-1 digit for each position without violating the constraint).

Of course, to obtain maximal compression we will use b = 256. To turn the input into an integer, we can use base conversion as well. If I wasn't lazy, I'd use a bijective base for the input, but for now I'm just prepending a 1 (to ensure there are no leading zeros) and then use the smallest possible base that such that all octets in the input are less than the base.

This base is then prepended to the encoding (so that the decoder knows which base to use) and separated from the remaining number by a 0-octet (this works because the remaining number will never start with a zero). As a minor optimisation, the empty string remains an empty string.

The reason I haven't computed an exact score above is that I'm only computing an upper bound for how long each input will be based on its length and its maximal octet. However, for these two parameters, there will often be two different output lengths, and I haven't bothered figuring out yet where the tipping point between them occurs. I've also used the length of usual base 255 instead of bijective base 255 to estimate that length, which is again slightly bigger than it needs to be. The exact Mathematica code I used for the computation is the following:

num[l_, 1] = 0;
num[l_, base_] := num[l, base] = base^l - Sum[num[l, b], {b, base - 1}]
  num[l, b]*(r + 1)*(2 + IntegerLength[2*b^l - 1, 255])/256^l, 
  {r, 0, 9}, {l, 1, 99}, {b, 2, 256}

num[l, b] should give the the number of strings of length l with maximum octet b-1 (except for b == 1, where I've hardcoded it to 0 because I'm always using at least base 2).

  • \$\begingroup\$ "Assuming that on average a string of length N cannot be encoded in less than (r+1)*N octets at radiation level r." I see no reason for this to be true. I wouldn't be surprised to see an encoding scheme existing which is O(N + r). \$\endgroup\$
    – orlp
    Commented Feb 23, 2017 at 15:19
  • 1
    \$\begingroup\$ @orlp I'm not seeing how that would be possible, but I'm looking forward to being proven wrong. :) \$\endgroup\$ Commented Feb 23, 2017 at 15:23
  • \$\begingroup\$ Nice idea. I don't know CJam, but from your description, it sounds like you're prepending the base to the encoded data. If that's the case, is there a problem if there are characters deleted from the prepended data? (That's the mistake I made that @Leo pointed out, that I had to fix in my solution.) \$\endgroup\$ Commented Feb 26, 2017 at 18:40
  • \$\begingroup\$ @MitchellSpector the base is prepended before repeating each character r+1 times. So the base is radiation safe as well. \$\endgroup\$ Commented Feb 26, 2017 at 18:48
  • \$\begingroup\$ That's good -- that's what I ended up doing in my solution as well. You just need to be sure that the decoder can decode the prepended data before knowing what the base is. \$\endgroup\$ Commented Feb 26, 2017 at 18:53

bash + GNU utilities, score 294506 283468

Edit 1: Fixes a problem that @Leo noticed -- thank you!

Edit 2: Improved the encoding method for the radiation parameter, for a better score.

Encoder (97 bytes):

for((j=0;j<$1;j++)){ p+=p\;;}
(sed 's/\(.\)\1/\1a\1a/g'<<<$1;cat)|xxd -p -c1|sed ${p-;}|xxd -r -p

Decoder (121 bytes):

read n
n=`sed 's/\(.\)\1*/\1/g'<<<$n`
sed -r "s/( ..)\1{0,${n//a}}/\1/g"<<<' 0a '`xxd -p -c1`|sed 's/^ [^ ]*//'|xxd -r -p

For the encoder: Octet sequence passed as characters in stdin, radiation parameter r passed as an argument.

For the decoder: Input passed as characters in stdin.

For both: Output on stdout.

The encoder prepends to the input data the digits of r, with a character 'a' inserted between each pair of consecutive identical digits, followed by a single newline. It then copies all the input (starting with the prepended characters), replacing each character by r+1 copies of that character.

The decoder undoes this, going through each of the remaining characters x in its input, skipping up to r consecutive identical copies of x following x, and printing what remains. The prepended data has no repeated characters, so it can be decoded before r is known. At that point, r is known, and that value is needed to decode the rest of the data correctly.

You can check that this works even if the original input has repeated identical characters.

Score computation:

Suppose the input has length L and the radiation parameter is r (which is at most 9 for the scoring computation, so it fits in one digit and therefore has no consecutive repeated characters). The prepended data is 2 bytes (digit, newline), so the output is (r+1)(L+2) bytes for the encoded stream.

So g(L,r) = (r+1)(L+2).

It follows that the total score can be computed as

enter image description here

  • \$\begingroup\$ What if the dropped octet is the first one? The decoder wouldn't have an r to read \$\endgroup\$
    – Leo
    Commented Feb 25, 2017 at 9:19
  • \$\begingroup\$ @Leo Your're right. I'll look at fixing that tomorrow -- it's too late tonight. Thanks for spotting it. \$\endgroup\$ Commented Feb 25, 2017 at 9:29
  • \$\begingroup\$ @Leo I think it's fixable by including r+1 copies of each of the digits of r, followed by r+1 newlines. If that's correct, the score won't go up by very much. \$\endgroup\$ Commented Feb 25, 2017 at 9:36
  • \$\begingroup\$ Something like that should work. I think you should take some additional measures to make sure that it works properly with higher radiations (e.g. a radiation of 222), but fortunately the score is computed just over radiations 0-9, so it won't be affected much. P.S. I was thinking of implementing this same encoding, that's why I spotted the error right away ;) \$\endgroup\$
    – Leo
    Commented Feb 25, 2017 at 9:49
  • \$\begingroup\$ @Leo Yes, the fix works for all values for the radiation, even though the score only takes into account radiation values of at most 9. \$\endgroup\$ Commented Feb 28, 2017 at 23:36

Perl + Math::{ModInt, Polynomial, Prime::Util}, score ≤ 92819

$m=Math::Polynomial;sub l{($n,$b,$d)=@_;$n||$d||return;$n%$b,l($n/$b,$b,$d&&$d-1)}sub g{$p=$m->interpolate([grep ref$_[$_],0..$map{$p->evaluate($_)}0..$}sub p{prev_prime(128**$s)}sub e{($_,$r)=@_;length||return'';$s=$r+1;s/^[␀␁]/␁$&/;@l=map{mod($_,p$s)}l(Math::BigInt->from_bytes($_),p$s);$@l+$r>p($s)&&return e($_,$s);$a=0;join'',map{map{chr$_+$a}l($_->residue,128,$s,($a^=128))}g(@l)}sub d{@l=split/([␀-␡]+)/,$_[0];@l||return'';$s=vecmax map length,@l;@l=g map{length==$s&&mod($m->new(map{ord()%128}split//)->evaluate(128),p$s)}@l;$$_=$m->new(map{$_->residue}@l)->evaluate(p$s)->to_bytes;s/^␁//;$_}

Control pictures are used to represent the corresponding control character (e.g. is a literal NUL character). Don't worry much about trying to read the code; there's a more readable version below.

Run with -Mbigint -MMath::ModInt=mod -MMath::Polynomial -MNtheory=:all. -MMath::Bigint=lib,GMP is not necessary (and thus not included in the score), but if you add it before the other libraries it'll make the program run somewhat faster.

Score calculation

The algorithm here is somewhat improvable, but would be rather harder to write (due to Perl not having the appropriate libraries). Because of this, I made a couple of size/efficiency tradeoffs in the code, on the basis that given that bytes can be saved in the encoding, there's no point in trying to shave off every point from golfing.

The program consists of 600 bytes of code, plus 78 bytes of penalties for command-line options, giving a 678 point penalty. The rest of the score was calculated by running the program on the best-case and worst-case (in terms of output length) string for every length from 0 to 99 and every radiation level from 0 to 9; the average case is somewhere in between, and this gives bounds on the score. (It's not worth trying to calculate the exact value unless another entry comes in with a similar score.)

This therefore means that the score from encoding efficiency is in the range 91100 to 92141 inclusive, thus the final score is:

91100 + 600 + 78 = 91778 ≤ score ≤ 92819 = 92141 + 600 + 78

Less-golfed version, with comments and test code

This is the original program + newlines, indentation, and comments. (Actually, the golfed version was produced by removing newlines/indentation/comments from this version.)

use 5.010;                    # -M5.010; free
use Math::BigInt lib=>'GMP';  # not necessary, but makes things much faster
use bigint;                   # -Mbigint
use Math::ModInt 'mod';       # -MMath::ModInt=mod
use Math::Polynomial;         # -MMath::Polynomial
use ntheory ':all';           # -Mntheory=:all
use warnings;                 # for testing; clearly not necessary

### Start of program

$m=Math::Polynomial;          # store the module in a variable for golfiness

sub l{ # express a number $n in base $b with at least $d digits, LSdigit first
    # Note: we can't use a builtin for this because the builtins I'm aware of
    # assume that $b fits into an integer, which is not necessarily the case.

sub g{ # replaces garbled blocks in the input with their actual values
    # The basic idea here is to interpolate a polynomial through all the blocks,
    # of the lowest possible degree. Unknown blocks then get the value that the
    # polynomial evaluates to. (This is a special case of Reed-Solomon coding.)
    # Clearly, if we have at least as many ungarbled blocks as we did original
    # elements, we'll get the same polynomial, thus we can always reconstruct
    # the input.
    # Note (because it's confusing): @_ is the input, $_ is the current element
    # in a loop, but @_ is written as $_ when using the [ or # operator (e.g.
    # $_[0] is the first element of @_.
    # We waste a few bytes of source for efficiency, storing the polynomial
    # in a variable rather than recalculating it each time.
    $p=$m->interpolate([grep ref$_[$_],0..$#_],[grep ref,@_]);
    # Then we just evaluate the polynomial for each element of the input.

sub p{ # determines maximum value of a block, given (radiation+1)
    # We split the input up into blocks. Each block has a prime number of
    # possibilities, and is stored using the top 7 bits of (radiation+1)
    # consecutive bytes of the output. Work out the largest possible prime that
    # satisfies this property.

sub e{ # encoder; arguments: input (bytestring), radiation (integer)
    ($_,$r)=@_; # Read the arguments into variables, $_ and $r respectively
    length||return''; # special case for empty string
    $s=$r+1; # Also store radiation+1; we use it a lot
    # Ensure that the input doesn't start with NUL, via prepending SOH to it if
    # it starts with NUL or SOH. This means that it can be converted to a number
    # and back, roundtripping correctly.
    s/^[␀␁]/␁$&/; #/# <- unconfuse Stack Exchange's syntax highlighting
    # Convert the input to a bignum, then to digits in base p$s, to split it
    # into blocks.
    # Encoding can reuse code from decoding; we append $r "garbled blocks" to
    # the blocks representing the input, and run the decoder, to figure out what
    # values they should have.
    # Our degarbling algorithm can only handle at most p$s blocks in total. If
    # that isn't the case, try a higher $r (which will cause a huge increase in
    # $b and a reduction in @l).
    @l+$r>p($s)&&return e($_,$s);
    # Convert each block to a sequence of $s digits in base 128, adding 128 to
    # alternating blocks; this way, deleting up to $r (i.e. less than $s) bytes
    # will preserve the boundaries between each block; then convert that to a
    # string
    $a=0; # we must initialize $a to make this function deterministic

sub d{ # decoder: arguments; encdng (bytestring)
    # Reconstruct the original blocks by looking at their top bits
    @l||return''; # special case for empty string
    # The length of the longest block is the radiation parameter plus 1 (i.e.
    # $s). Use that to reconstruct the value of $s.
    $s=vecmax map length,@l;
    # Convert each block to a number, or to undef if it has the wrong length.
    # Then work out the values for the undefs.
    @l=g map{
        # Convert blocks with the wrong length to undef.
            # Convert other blocks to numbers, via removing any +128 and then
            # using Math::Polynomial to convert the digit list to a number.
            mod($m->new(map{ord()%128}split// #/# <- fix syntax highlighting
    # Remove the redundant elements at the end; now that they've reconstructed
    # the garbled elements they have no further use.
    # Convert @l to a single number (reversing the conversion into blocks.)
        # Convert that number into a string.
    # Delete a leading SOH.
    s/^␁//;  #/# <- unconfuse Stack Exchange's syntax highlighting
    # Finally, return the string.

### Testing code
use Encode qw/encode decode/;

# Express a string using control pictures + IBM437, to make binary strings
# easier for a human to parse
sub format_string {
    $_ = decode("Latin-1", $_);
    s/[\0-\x1f]/chr (0x2400 + ord $&)/eag;
    s/\x7f/chr 0x2421/eag;
    s/[ -~\x80-\xff]/decode("IBM437",$&)/eag;

sub test {
    my ($string, $radiation, $samples) = @_;
    say "Input: ", format_string($string);
    my $encoding = e($string, $radiation);
    say "Encoding: ", format_string($encoding);
    say "Input length ", length($string), ", encoding length ", length($encoding), ", radiation $radiation";
    my $decoding = d($encoding);
    $decoding eq $string or die "Mistake in output!";
    say "Decoding: ", format_string($decoding), " from ",

    # Pseudo-randomly generate $samples radiation-damaged versions.
    srand 1;
    for my $i (1..$samples) {
        my $encdng = $encoding;
        for my $r (1..$radiation) {
            substr $encdng, int(rand(length $encdng)), 1, "";
        my $newdecoding = d($encdng);
        say "Decoding: ", format_string($newdecoding), " from ",
        $newdecoding eq $string or die "Mistake in output!";

    say "";
    length $encoding;

test "abcdefghijklm", 1, 10;
test "abcdefghijklm", 2, 10;
test "abcdefghijklm", 5, 10;
test "abcdefghijklm", 10, 10;
test "\0\0\0\0\0", 1, 10;
test "\5\4\3\2\1", 2, 10;
test "a", 10, 10;

my %minlength = ();
my %maxlength = ();

for my $length (0..99) {
    my ($min, $max) = ("", "");
    $length and ($min, $max) =
        ("\2" . "\0" x ($length - 1), "\1" . "\377" x ($length - 1));
    for my $radiation (0..9) {
        $minlength{"$length-$radiation"} = test $min, $radiation, 1;
        $maxlength{"$length-$radiation"} = test $max, $radiation, 1;

say "Minimum score: ", vecsum values %minlength;
say "Maximum score: ", vecsum values %maxlength;


Simplifying the problem

The basic idea is to reduce this "deletion coding" problem (which is not a widely-explored one) into an erasure coding problem (a comprehensively explored area of mathematics). The idea behind erasure coding is that you're preparing data to be sent over an "erasure channel", a channel that sometimes replaces the characters it sends with a "garble" character that indicates a known position of an error. (In other words, it's always clear where corruption has occurred, although the original character is still unknown.) The idea behind that is pretty simple: we divide the input up into blocks of length (radiation + 1), and use seven out of the eight bits in each block for data, whilst the remaining bit (in this construction, the MSB) alternates between being set for an entire block, clear for the entire next block, set for the block after that, and so on. Because the blocks are longer than the radiation parameter, at least one character from each block survives into the output; so by taking runs of characters with the same MSB, we can work out which block each character belonged to. The number of blocks is also always greater than the radiation parameter, so we always have at least one undamaged block in the encdng; we thus know that all blocks that are longest or tied for longest are undamaged, allowing us to treat any shorter blocks as damaged (thus a garble). We can also deduce the radiation parameter like this (it's the length of an undamaged block, minus 1).

Erasure coding

As for the erasure coding part of the problem, this uses a simple special case of the Reed-Solomon construction. This is a systematic construction: the output (of the erasure coding algorithm) is equal to the input plus a number of extra blocks, equal to the radiation parameter. We can calculate the values needed for these blocks in a simple (and golfy!) way, via treating them as garbles, then running the decoding algorithm on them to "reconstruct" their value.

The actual idea behind the construction is also very simple: we fit a polynomial, of the minimum possible degree, to all the blocks in the encoding (with garbles interpolated from the other elements); if the polynomial is f, the first block is f(0), the second is f(1), and so on. It's clear that the degree of the polynomial will equal the number of blocks of input minus 1 (because we fit a polynomial to those first, then use it to construct the extra "check" blocks); and because d+1 points uniquely define a polynomial of degree d, garbling any number of blocks (up to the radiation parameter) will leave a number of undamaged blocks equal to the original input, which is enough information to reconstruct the same polynomial. (We then just have to evaluate the polynomial to ungarble a block.)

Base conversion

The final consideration left here is to do with the actual values taken by the blocks; if we do polynomial interpolation on the integers, the results may be rational numbers (rather than integers), much larger than the input values, or otherwise undesirable. As such, instead of using the integers, we use a finite field; in this program, the finite field used is the field of integers modulo p, where p is the largest prime less than 128radiation+1 (i.e. the largest prime for which we can fit a number of distinct values equal to that prime into the data part of a block). The big advantage of finite fields is that division (except by 0) is uniquely defined and will always produce a value within that field; thus, the interpolated values of the polynomials will fit into a block just the same way that the input values do.

In order to convert the input into a series of block data, then, we need to do base conversion: convert the input from base 256 into a number, then convert into base p (e.g. for a radiation parameter of 1, we have p = 16381). This was mostly held up by Perl's lack of base conversion routines (Math::Prime::Util has some, but they don't work for bignum bases, and some of the primes we work with here are incredibly large). As we're already using Math::Polynomial for polynomial interpolation, I was able to reuse it as a "convert from digit sequence" function (via viewing the digits as the coefficients of a polynomial and evaluating it), and this works for bignums just fine. Going the other way, though, I had to write the function myself. Luckily, it isn't too hard (or verbose) to write. Unfortunately, this base conversion means that the input is typically rendered unreadable. There's also an issue with leading zeroes; this program uses the simple (but not perfectly efficient) method of prepending a 1 octet to the program if it starts with a 0 or 1 octet (and reversing the translation when decoding), thus avoiding the issue altogether.

It should be noted that we can't have more than p blocks in the output (otherwise the indexes of two blocks would become equal, and yet possibly need to produce different outputs form the polynomial). This only happens when the input is extremely large. This program solves the issue in a very simple way: increasing radiation (which makes the blocks larger and p much larger, meaning we can fit much more data in, and which clearly leads to a correct result).

One other point worth making is that we encode the null string to itself, because the program as written would crash on it otherwise. It's also clearly the best possible encoding, and works no matter what the radiation parameter is.

Potential improvements

The main asymptotic inefficiency in this program is to do with the use of modulo-prime as the finite fields in question. Finite fields of size 2n exist (which is exactly what we'd want here, because the blocks' payload sizes are naturally a power of 128). Unfortunately, they're rather more complex than a simple modulo construction, meaning that Math::ModInt wouldn't cut it (and I couldn't find any libraries on CPAN for handling finite fields of non-prime sizes); I'd have to write an entire class with overloaded arithmetic for Math::Polynomial to be able to handle it, and at that point the byte cost might potentially overweigh the (very small) loss from using, e.g., 16381 rather than 16384.

Another advantage of using power-of-2 sizes is that the base conversion would become much easier. However, in either case, a better method of representing the length of the input would be useful; the "prepend a 1 in ambiguous cases" method is simple but wasteful. Bijective base conversion is one plausible approach here (the idea is that you have the base as a digit, and 0 as not a digit, so that each number corresponds to a single string).

Although the asymptotic performance of this encoding is very good (e.g. for an input of length 99 and a radiation parameter of 3, the encoding is always 128 bytes long, rather than the ~400 bytes that repetition-based approaches would get), its performance is less good on short inputs; the length of the encoding is always at least the square of the (radiation parameter + 1). So for very short inputs (length 1 to 8) at radiation 9, the length of the output is nonetheless 100. (At length 9, the length of the output is sometimes 100 and sometimes 110.) Repetition-based approaches clearly beat this erasure-coding-based approach on very small inputs; it might be worth changing between multiple algorithms based on the size of the input.

Finally, it doesn't really come up in the scoring, but with very high radiation parameters, using a bit of every byte (⅛ of the output size) to delimit blocks is wasteful; it would be cheaper to use delimiters between the blocks instead. Reconstructing the blocks from delimiters is rather harder than with the alternating-MSB approach, but I believe it to be possible, at least if the data is sufficiently long (with short data, it can be hard to deduce the radiation parameter from the output). That would be something to look at if aiming for an asymptotically ideal approach regardless of the parameters.

(And of course, there could be an entirely different algorithm that produces better results than this one!)


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.