The problem is a follow-up to the problem MaximumSubArraySum, to provide a result that also contains the sub-array of interest, not just the sum.
This sort of problem may come up in real-life optimisation tasks.
Test cases in Scala
assert(computeForArrayClearKadane(SampleArray).array.toList == BestSolution) assert(computeForArrayClearKadane(SampleArray).sumSoFar == 6)
Algorithm in Scala
28 lines of Scala (version 2.13), showing how concise Scala can be!
Please read the MaximumSubArraySum problem explanation for the mathematics - for this problem here, we will focus on how to attach the source array and how to get it the most efficiently.
Here, we use a 'State Machine', which lets us group together various values. (this is © from www.scala-algorithms.com)
Once we have iterated through the whole array, we have a 'view' of a set of best results ending at each position. Then we pick the best one overall with `maxBy`, and from that, we extract the resulting array.
Scala concepts & Hints
Pattern matching in Scala lets you quickly identify what you are looking for in a data, and also extract it.
scanLeft and scanRight
Scala's `scan` functions enable you to do folds like foldLeft and foldRight, while collecting the intermediate results
Stack safety is present where a function cannot crash due to overflowing the limit of number of recursive calls.
This function will work for n = 5, but will not work for n = 2000 (crash with java.lang.StackOverflowError) - however there is a way to fix it :-)
In Scala Algorithms, we try to write the algorithms in a stack-safe way, where possible, so that when you use the algorithms, they will not crash on large inputs. However, stack-safe implementations are often more complex, and in some cases, overly complex, for the task at hand.
.viewsyntax creates a structure that mirrors another structure, until "forced" by an eager operation like .toList, .foreach, .forall, .count.