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⚡ Bolt: [O(N^3) to O(N^2) nested loop optimization in SubjectTrendLineChart] #206

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⚡ Bolt: [O(N^3) to O(N^2) nested loop optimization in SubjectTrendLineChart] #206

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8 changes: 6 additions & 2 deletions android/app/src/main/java/com/clhs/score/ui/SubjectTrendLineChart.kt
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Original file line number Diff line number Diff line change
Expand Up @@ -135,10 +135,14 @@ fun SubjectTrendLineChart(
val currentPoints = pointsByIndex[indices[i]]!!
val nextPoints = pointsByIndex[indices[i+1]]!!

// Optimization: Precompute subjects to avoid O(N^3) complexity
val currentSubjects = currentPoints.map { it.third }.toSet()
val nextSubjects = nextPoints.map { it.third }.toSet()

currentPoints.forEach { p1 ->
nextPoints.forEach { p2 ->
val p1HasSuccessor = nextPoints.any { it.third == p1.third }
val p2HasPredecessor = currentPoints.any { it.third == p2.third }
val p1HasSuccessor = nextSubjects.contains(p1.third)
val p2HasPredecessor = currentSubjects.contains(p2.third)
Comment on lines +139 to +145

@gemini-code-assist gemini-code-assist Bot Jun 18, 2026

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medium

While the introduction of Set lookups successfully reduces the complexity from $O(N^3)$ to $O(N^2)$, we can optimize this further by hoisting independent computations out of the nested loops:

  1. p1HasSuccessor only depends on p1 and nextSubjects. It can be hoisted to the outer loop, reducing its lookups from $N \times M$ to $N$.
  2. p2HasPredecessor only depends on p2 and currentSubjects. We can precompute this for all nextPoints before entering the loops, reducing its lookups from $N \times M$ to $M$.
  3. mapTo(HashSet()) can be used instead of map { ... }.toSet() to avoid intermediate list allocations.

This reduces the total number of set lookups from $2 \times N \times M$ to just $N + M$, which is highly beneficial for drawing/rendering performance.

Suggested change
val currentSubjects = currentPoints.map { it.third }.toSet()
val nextSubjects = nextPoints.map { it.third }.toSet()
currentPoints.forEach { p1 ->
nextPoints.forEach { p2 ->
val p1HasSuccessor = nextPoints.any { it.third == p1.third }
val p2HasPredecessor = currentPoints.any { it.third == p2.third }
val p1HasSuccessor = nextSubjects.contains(p1.third)
val p2HasPredecessor = currentSubjects.contains(p2.third)
val currentSubjects = currentPoints.mapTo(HashSet()) { it.third }
val nextSubjects = nextPoints.mapTo(HashSet()) { it.third }
val p2HasPredecessorList = nextPoints.map { currentSubjects.contains(it.third) }
currentPoints.forEach { p1 ->
val p1HasSuccessor = nextSubjects.contains(p1.third)
nextPoints.forEachIndexed { p2Index, p2 ->
val p2HasPredecessor = p2HasPredecessorList[p2Index]


val isDifferentSubjectConnection = (!p1HasSuccessor || !p2HasPredecessor) && p1.third != p2.third
val isSameSubjectGap = p1.third == p2.third && (p2.first - p1.first > 1)
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