# APL Problem Solving Competition 2023

This year I participated in the APL Problem Solving Competition organized by Dyalog Ltd, I will document my thoughts about the problems I solved here.

# Part 1: Bioinformatics

the first problem starts off pretty tame: write a dfn that converts a DNA sequence to RNA, where both input and outputs are character arrays.

The solution doesn’t need much explanation, it’s just replacing the 'T's with 'U's.

rna  ← {'U'@('T'∘=)⍵}

I had a little more fun with the next one, as I avoided writing down two separate look up tables by arranging my data properly:

the statement calls for a dfn that returns the reverse complement of a DNA sequence.

• First we reverse the sequence: {⌽⍵}
• Then we find look up each element in the string 'ACGT': {'ACGT'⍳⌽⍵}
• The reverse of the same string dictates the replacements: {(⊂'ACGT'⍳⌽⍵)⌷⌽'ACGT'}

With a little massaging we end up with the final solution.

revc ← {'ACGT'(⊂⍤⍳⌷⌽⍤⊣)⌽⍵}

This task calls for a dfn that returns the protein coded by a RNA sequence, terminating at the first stop codon. Setting up a lookup table is a logical first step:

t ← ↑('UUU' 'F')('CUU' 'L')('AUU' 'I')('GUU' 'V')
t⍪← ↑('UUC' 'F')('CUC' 'L')('AUC' 'I')('GUC' 'V')
...
t⍪← ↑('UGG' 'W')('CGG' 'R')('AGG' 'R')('GGG' 'G')
r_cod amm ← ↓⍉t

The helper function r_trs does the heavy lifting, splitting the sequence in codons of 3 (x←{↓(⌊3÷⍨≢⍵)3⍴⍵}r), looking up each one (y←r_cod⍳x) and assinging the corresponding amino-acid (amm⌷⍨∘⊂y).

the whole function can be written tacitly, and the final solution just applies it to it’s input and truncates the result at the first '$'. r_trs ← amm⌷⍨∘⊂r_cod⍳{↓(⌊3÷⍨≢⍵)3⍴⍵} prot ← {'$'((¯1+⍳⍨)↑⊢)r_trs⍵}

This was a parsing problem, of which I’ve done entirely too many doing Advent of Code in APL last year: it all boils down to partitioning and enclosing and partition-enclosing.

We need to write a dfn that accepts the path to a fasta file and returns a list of header-sequence pairs corresponding to the DNA sequences stored in the file.

For reference, a fasta file looks something like this:

>Rosalind_99
AGCCATGTAGCTAACTCAGGTTACATGGGGATGACCCCGCGACTTGGA
TTAGAGTCTCTTTTGGAATAAGCCTGAATGATCCGAGTAGCATCTCAG
>Rosalind_2748
ATCAGGCTACCGTGTTTGCGGACGGGGGCTTAATCTAGCTTCTCATCTCAGCGACGTCTC
CTTGTTGGCACAGCGGTGGCAGGAGGTCCCCGCCGAGGAGCACATCGACCTTTCGGTGTA
...

The lines starting with > are headers, and the following lines contain the DNA sequences.

The solution is fairly pedestrian:

• Read the file’s lines with ⊃⎕NGET⍵1
• Identify the header lines with ('>'=⊃¨), and use them to partition-enclose the input.
• For each chunk:
• ,/1↓⍵ concatenates all but the header
• ⊂' '(1↓(¯1+⍳⍨)↑⊢)⊃⍵ gets the header up to the first space character.

The task is to write a function that takes a fasta file, extracts the first (only) DNA sequence, and returns all the proteins coded by it, beginning with a ‘M’ amino-acid and ending with a stop codon. I approached the problem by writing a helper function that would turn a sequence of DNA into the amino-acid sequences of it’s 6 reading frames.

Our final solution will look like this (for some definition of crf and aas):

• We just defined readFASTA, which will give us the sequences.
• ⊃⌽⊃ takes the last element in the first header-sequence pair.
• aas will be defined later, it outputs the amino-acid sequences for the six reading frames of the DNA sequence.
• crf will be defined later, it accepts a list of amino-acid sequences and outputs their subsequences that begin with M and end right before a $. ### Defining aas Here I had the choice between the easy and intuitive solution by composition, or the “smart” solution that probably has no real benefit in this case. I obviously chose the latter. What I could have done was to extract the reading frames with the help of revc, then convert each DNA sequence into RNA with rna, finally transcribe into amino-acids with r_trs: It would have looked something like this: aas ← r_trs¨rna¨(,0 1 2∘.↓,⍥⊂∘revc⍨) however, RNA and DNA are dual representations of the same data, so I opted to convert my lookup table into DNA representation, and write a function to parallel r_trs: this saves us the RNA conversion, and for big inputs it may perform better (though probably not by a significant amount). d_cod ← 'T'@('U'∘=)¨r_cod d_trs ← amm⌷⍨∘⊂d_cod⍳{↓(⌊3÷⍨≢⍵)3⍴⍵} aas ← d_trs¨(,0 1 2∘.↓,⍥⊂∘revc⍨) ### Retrospective In hindsight, I could have been a little smarter still: observing that the possible codons are just 3-digit numbers in the base 4 number system with digits 'ACGT', we can leverage the decode primitive. {1+4⊥⍉¯1+'ACGT'⍳(⌊3÷⍨≢⍵)3⍴⍵} can substitute the lookup into d_cod, and d_trs can be rewritten as d_trs ← amm⌷⍨∘⊂{1+4⊥⍉¯1+'ACGT'⍳(⌊3÷⍨≢⍵)3⍴⍵}, provided that amm contains the amino-acid sorted by the respective codon’s numeric value. There is no more need for the d_cod or r_cod tables at all! we could even parametrise the solution and pass either 'ACGT' or 'ACGU' to get both r_trs and d_trs. The final definition would have looked something like this: trs ← {amm[1+4⊥⍉¯1+⍺⍳(⌊3÷⍨≢⍵)3⍴⍵]} aas ← 'ACGT'∘trs¨(,0 1 2∘.↓,⍥⊂∘revc⍨) But we would still be doing duplicate work in the lookups, as well as giving up some opportunities for semplification: this is what it would look like after addressing those issues. amm ← 'KNKNTTTTRSRSIIMIQHQHPPPPRRRRLLLLEDEDAAAAGGGGVVVV$Y$YSSSS$CWCLFLF'
aas ← {{amm[1+4⊥⍉(⌊3÷⍨≢⍵)3⍴⍵]}¨,0 1 2∘.↓,⍥⊂∘(5-⌽)¯1+'ACGT'⍳⍵}

A nice insight comes from this layout: the last base is ignored in most codons, as can be observed in 4 4 4⍴amm (many rows are constant).

Speculation aside, to complete the task we would like - given a list of the amino-acids coded by the reading frames - to extract any subsequence of one of them, which starts with 'M' and ends just before a '$'. Now I find myself in a bit of an embarassing situation: I have two solutions to this task, which diverge at this point. I definitely submitted the first one, but I am not sure if I ended up overriding my submission with the second solution; as a result, I am not sure which version ended up being graded. I will just describe them both and worry about it some other time. ### Solution 1 We define a 1-modifier _f which takes a monadic function and applies it to each value in , then (expecting ⍺⍺ to return a list for each application) concatenates the results together. This is heavily inspired by rust’s flat_map. _f ← {⊃,/⍺⍺¨⍵} To construct the solution: • Partition each reading frame by$:
• '$'∘≠⊆⊢ is a tacit function that splits on$
• -'$'≠⊢/ returns 0 if the sequence ends in$, ¯1 otherwise. In the latter case, we need to drop the last chunk from the partitioning.
• (-'$'≠⊢/)↓'$'∘≠⊆⊢ puts them together to give the chunks that end just before $. • Given each chunk: • 'M'∘(=⊂⊢) partition-encloses at the positions of M. • ,⍨\⍤⌽ performs a reverse-catenate-scan in reverse (what a mouthful) which yields the subsequences starting with M and ending at the end of the chunk. What’s left is to do some plumbing with _f and make sure the results are unique: crf ← {∪'M'∘(,⍨\⍤⌽(=⊂⊢))_f((-'$'≠⊢/)↓'$'∘≠⊆⊢)_f⍵} ### Solution 2 The second solution is arguably a little more interesting, as it doesn’t directly translate from something you might write in a scalar language. • Concatenate all the reading frames together, separating with '$':
s d←(,/,(⊂(+\≢¨))),∘'$'¨⍵ • to avoid losing information, we also store the positions of the artificially inserted$s, which are available as the cumulative sum of the lengths.
• Find the positions of M and $in the merged input with 'M$'(⍸=)¨⊂s.
• Match each of the Ms to the corresponding $’s position with binary search: i←{↑⍺(⍵[1+⍵⍸⍺])}/ ... • note that there is always a$ at the end of input, so each M finds a corresponding $. • Remove any values where the stop codon was inserted in step 1: b e←¯1+↓(~i[2;]∊d)/i • Finally use the indices to select the relevant subsequences: ∪b↓¨e↑¨⊂s. Putting it all together, we get the following: crf ← { s d←(,/,(⊂(+\≢¨))),∘'$'¨⍵
b e←¯1+↓(~i[2;]∊d)/i←⊃{↑⍺(⍵[1+⍵⍸⍺])}/'M\$'(⍸=)¨⊂s
∪b↓¨e↑¨⊂s
}

### Retrospective

Looking back on it, the last line might have been better expressed as ∪b{s[⍺+⍳⍵-⍺]}¨e to avoid materializing too many big arrays, but the performance implications of it depend on the implementation.

# Part 2: Potpourri

The task is to write a dfn that computes or validates a vehicle identification number; there are quite a few details involved, but the long and short of it is that characters in the VIN contribute to a total score according to their value and position, and the score modulo 11 determines the correct value of the 9th digit.

The solution handles a few cases:

• if the VIN contains 16 legal characters, calculate what character can be placed at position 9 to make a valid VIN, and do so.
• if the VIN contains 17 legal characters, check that the 9th character matches the one that we calculate from the rest of the VIN.
• otherwise, return ¯1.
vinc ← ⎕D,⎕A~'IOQ'
vin ← {
~×/⍵∊vinc: ¯1
16=≢⍵: (calc(⊣@9)⊢)8(↑,'0',↓)⍵
17=≢⍵: (calc=9∘⌷)⍵
¯1
}

We delegate the messy part to the calc helper function, that takes a 17 character vector and returns the character that, placed at position 9, would make the VIN valid.

### Defining calc

I set up the tables:

• num contains the numeric values to substitute to the characters
• mul contains the values that digits at each position must be multiplied by.
num  ← ((65∘≤×1+9|83∘≤+-∘65)+(57∘≥×-∘48))⎕ucs vinc
mul  ← 10 0@8 9⊢17↑2↓⌽,⍨⍳10
calc ← {(⎕D,'X')⌷⍨1+11|mul+.×num⌷⍨⊂vinc⍳⍵}

The procedure is as follows:

• Look up each character’s value with num⌷⍨⊂vinc⍳⍵
• Since the score is calculated via a sum-of-products, the choice falls on inner product: mul +.× ...
• take the modulo and pick a digit: (⎕D,'X')⌷⍨1+11| ...

### Retrospective

the tables defined above look very bad and are not at all insightful, what I should really have written is:

num  ← (⍳9),(⍳8),((⍳9)~6 8),1↓⍳9
mul  ← (⊢,10 0 9,⊢)⌽1↓⍳8

… or even just the raw numbers, it’s not like we’re starved for hard disk space.

This task asked for a function that sorts strings representing software versions according to their release number. The structure of the input is either:

• a string in the format "[a-zA-Z]+-[a-zA-Z]+-[0-9]+\.[0-9]+\.[0-9]+"
• a list of strings in the above format.

The solution leverages total array ordering, and the bulk of the problem just boils down to parsing the numbers in the string.

parts ← (-∘~⍨/'.'⎕vfi⊃)@3('-'∘≠⊆⊢)
sortVersions ← {1=≡⍵: ,⊂⍵ ⋄ ((⊂∘⍋parts¨)⌷⊢)⍵}

If the input is a flat character array, then just return it wrapped in a 1-element list. If it’s a list of strings, split each entry by - into the fields (org, package, version). Then parse the three numbers in version using ⎕vfi: this suggests a nice extension of the problem statement: if one of the fields in version is not a number, ⎕vfi will return a 0 verification bit and a 0 result: this means that the expression result-~verification returns result if valid, ¯1 otherwise.

This allows us to sort versions like foo-bar-10.x.0 in between foo-bar-9.9.9 and foo-bar-10.0.0, which is kind of nice to have.

With that out of the way, we have a list of entries in the form:
org package (major minor patch).
Applying gives us a sorting permutation, which by the rules of total array ordering conforms to the problem statement.

The task is to write a dfn that, given a sorted list of coin denominations (positive integers) as left argument and a total amount (a positive integer) as right argument, returns a matrix, where each row contains the coefficients of a linear combination of the elements in the left argument which sums to the right argument. Of course, the matrix should be exhaustive and not contain any duplicates.

Another way to look at this is that it returns the tallest matrix res that satisfies the following constraints:

⍵∧.=res+.×⍺
res≡∪res

The solution I submitted is a classic recursive one, with just a little bit of care put into the performance of it:

makeChange ← {
0≠⍵|⍨∨/⍺: (0,≢⍺)⍴0
1=≢⍺: ⍪⍵÷⊃⍺
⊃⍪/(((⊂¯1↓⍺)∇¨⍵-(⊃⌽⍺)×⊢),¨⊢)0,⍳⌊⍵÷⊃⌽⍺
}
• It is a well known mathematical fact that for a finite set $$A$$ of integers, there exists a linear combination in $$A$$ with integer coefficients that sums to $$n$$ if and only if $$\text{GCD}(A) | n$$ (where the GCD of a finite set is given by the pairwise GCD reduction of the elements in some arbitrary ordering, and $$|$$ is the divisibility symbol). This allows us to immediately return (0,≢⍺)⍴0 if is not divisible by ∨/⍺, which prunes some branches.

• The second line handles the general base case, and it is only reached if there is exactly one trivial solution. We can avoid checking divisibility because the first line catches the case where 0≠⍵|⊃⍺.

• The third line does all the recursing:

• 0,⍳⌊⍵÷⊃⌽⍺ determines how many coins of the largest denomination can be taken away from .
• ⍵-(⊃⌽⍺)×⊢ calculates the amount left over from each operation.
• (⊂¯1↓⍺)∇¨⍵-(⊃⌽⍺)×⊢ recurses on each case, removing the largest denomination.
• a solution of the original problem can be formed by appending $$n$$ to a solution of the reduced problem that takes away $$n$$ coins of the largest denomination: therefore (((⊂¯1↓⍺)∇¨⍵-(⊃⌽⍺)×⊢),¨⊢) ... gives a list of matrices of the solutions
• ⊃⍪/ concatenates the solutions together.
• It may look suspicious that we’re not deduplicating the lines, but it can be proven that every solution gotten this way is already unique:
• the first two cases always return zero or one result, and are trivially unique.
• by inductive hypothesis, results returned by a recursive call are internally unique.
• results obtained by two different recursive calls are distinct, since they are joined with distinct values of $$n$$.

### Retrospective

After some thinking, I came up with a more interesting solution for this problem, which I didn’t submit as the competition had already closed: the idea is to reduce the amount of recursive calls as much as possible, by allowing the function to take an array right argument and process values of in parallel, sharing the recursive subproblems between them.

makeChange ← {
1≡≢⍺: (0=⍵|⍨⊃⍺)↑¨⍪¨⍵÷⊃⍺
d←0,¨⍳¨⌊⍵÷i←⊢/⍺
u←∪∊r←⍵-i×d
s←(¯1↓⍺)∇u
⊃⍣(0≡≢⍴⍵)⊢r{⊃⍪/s[u⍳⍺],¨⍵}¨d
}
• In the base case, if only has one element, we provide the trivial solution to each element in as a 0 1 or 1 1 matrix.
• otherwise, we generate a list of unique subproblems, recurse, then use them to solve the original problem.
• d←0,¨⍳¨⌊⍵÷i←⊢/⍺ generates a list of lists, each containing the amounts of coins of the biggest denomination that can be taken from each element of .
• r←⍵-i×d generates the nested list of amounts left over from each operation
• u←∪∊r flattens and deduplicates this list, giving the list of subproblems to recurse on.
• s←(¯1↓⍺)∇u gives the list of solutions to each subproblem
• {⊃⍪/s[u⍳⍺],¨⍵} looks up each element of in the list of subproblems and gives the corresponding solution, then joins it to and concatenates the results.
• ⊃⍣(0≡≢⍴⍵) discloses the result if the right argument was a scalar, so that the result is conformant with the problem statement.

The task is to write a procedure partition that returns sliding windows of the right argument, with the size, strides and starting position given by the left argument.

partition ← {
1 0∊⍨≡⍺: (⊂,⍺)∇⍵
(≢⍴⍵)<≢⊃⍺: 0↑⊂(⊃⍺)⍴⍬
s m o←(≢⍴⍵)(⊢,⍨1⍴⍨-∘≢)¨3↑⍺,⍬⍬
,⊂⍤((⊃⍺)∘⍴)⍤(≢s)⊢⍵[(¯1+⍳s)∘.+⍨(⊂o)+(⊂m)×¯1+⍳0⌈1+⌊m÷⍨(⍴⍵)-o+s-1]
}

We start by parsing the left argument, case by case.

• If is simple, we enclose it (it will be used to construct s later): (⊂,⍺)∇⍵
• If is nested:
• If ≢⊃⍺ is greater than the rank of , we are being asked for a window that can’t be found in the array, and we return the empty list, however we also set the fill element to be the desired shape, to maintain the invariant (⊃⍺)≡⍴⊃⍺partition⍵.
• The rest of our code assumes input in the format s m o where:
• s designates the shape of the windows.
• m designates the strides of the windows along each dimension.
• o designates the origin of the first window.
Each of these may be underspecified (that is, given as a list that is shorter than the rank of , or omitted completely. in this case, the implied value for the omitted leading dimensions is 1. To make sure our arrays are in this format:
• 3↑⍺,⍬⍬: Pad with instances of up to 3 elements:
• (≢⍴⍵)(⊢,⍨1⍴⍨-∘≢)¨ ...: Prepend 1s to each element, up to ≢⍴⍵.

Then, our strategy is to generate indices into to build up the resulting windows:

• (⍴⍵)-o+s-1: The counts of the distinct (non-trivial) origins for a window of shape s along each axis: each window has to start at a position pos such that: ∧/ (o ≤ pos) , pos ≤ (⍴⍵)-s. If no windows fit along an axis, the corresponding component is negative.
• 0⌈1+⌊m÷⍨ ...: Divide by the strides to get the counts of windows actually in the result: clamp negative components up to 0.
• (⊂o)+(⊂m)×¯1+⍳ ...: Generate the origin of each window as the sum of the base position o and an offset.
• (¯1+⍳s)∘.+⍨ ...: Sum each window’s origin with each offset in the shape of the window with outer product. the commute is used to make sure the rank ≢s cells of the result correspond to windows.
• ⍵[ ... ]: Index into with the generated indices.
• ⊂⍤((⊃⍺)∘⍴)⍤(≢s) ...: Pick the cells of rank ≢s (each corresponding to a window) and enclose them after giving them the requested shape, finally unravel the result.

### Retrospective:

I didn’t feel satisfied with this solution, mainly because it uses way too many enclosed arrays as indices. Here it is, rewritten to convert to scalar indices in ravel order. I took the opportunity to modify a couple other things I wasn’t happy with:

• Take ⎕IO into account instead of assuming ⎕IO←1.
• Use to conditionally enclose instead of using recursion.
I ← {⍵⊤⎕IO-⍨⍳×/⍵}
partition ← {
(≢⍴⍵)<≢⊃a←⊆⍺: 0↑⊂(⊃a)⍴⍬
s m o←(-r)↑¨(⊂(r←≢sw←⍴⍵)⍴1),¨3↑a,⍬⍬
or←sw⊥m×⍤0 1I 0⌈1+⌊m÷⍨sw-s+o-⎕IO
⊂⍤¯1⊢((≢or),⊃a)⍴(⊂⎕IO+(or+sw⊥o-⎕IO)∘.+sw⊥I s)⌷,⍵
}

The result is a little longer, but arguably simpler:

• 0⌈1+⌊m÷⍨sw-s+o-⎕IO is exactly as before, but accounts for ⎕IO.
• I ← {⍵⊤⎕IO-⍨⍳×/⍵} defines a function equivalent to {⍉⎕IO-⍨↑,⍳⍵} but doesn’t use nested arrays. That is, it generates a matrix whose columns are the 0-based indices into an array of shape , given in ravel order.
• or←sw⊥m×⍤0 1I ... generates the 0-based indices into ,⍵ of the origins of the windows. (the pattern sw⊥ ... is used to convert a matrix whose columns are 0-based indices (or offsets) into to a list of corresponding indices (or offsets) into ,⍵)
• sw⊥I s gives the offset in ,⍵ of each element in a window from the origin of the same window.
• (⊂⎕IO+(or+sw⊥o-⎕IO)∘.+sw⊥I s)⌷,⍵ builds up windows of indices and uses them to pick from ,⍵
• ⊂⍤¯1⊢((≢or),⊃a)⍴ ... adjusts the shape of the windows and encloses the results.

If nothing else, this shows my issue with ⎕IO: as soon as you start doing arithmetic on indices it gets in the way. If ⎕IO was always 0, the solution could be simplified to:

I ← {⍵⊤⍳×/⍵}
partition ← {
(≢⍴⍵)<≢⊃a←⊆⍺: 0↑⊂(⊃a)⍴⍬
s m o←(-r)↑¨(⊂(r←≢sw←⍴⍵)⍴1),¨3↑a,⍬⍬
or←sw⊥m×⍤0 1I 0⌈1+⌊m÷⍨sw-s+o
⊂⍤¯1⊢((≢or),⊃a)⍴(⊂(or+sw⊥o)∘.+sw⊥I s)⌷,⍵
}

It also exemplifies how keeping the data representation flat can simplify the reasoning, as well as improving the performance: the new version is about 28x faster than the old one on 3 3 3 partition 25 25 25⍴⎕A.