Task Scheduler
Detailed guide and Python implementation for the 'Task Scheduler' problem.
1. Concept Overview
The 'Task Scheduler' problem is a key challenge in the Heap / Priority Queue section.
This implementation focuses on easy-level logic in Python.
We prioritize technical accuracy and code readability in our provided solutions.
2. Real-World Applications
3. Visual Intuition
Visualizing the logic flow for Task Scheduler.
4. Prerequisites
5. Step-by-Step Thinking
1. Understand the problem
Read the problem statement for Task Scheduler carefully.
2. Formulate brute force
Draft a simple iterative solution.
3. Identify inefficiency
Look for redundant calculations.
4. Optimize search path
Use hashing or sorting to speed up the process.
5. Final Implementation
Clean up the code for production standards.
Problem Statement
You are given an array of CPU tasks, each represented by a letter A to Z, and a cooling interval n. Each cycle or interval allows the completion of one task. Tasks can be completed in any order, but there's a constraint: identical tasks must be separated by at least n intervals due to cooling requirements.
Return the minimum number of intervals required to complete all tasks.
Write a function leastInterval(tasks: List[str], n: int) -> int.
- •1 <= len(tasks) <= 10^4
- •tasks[i] is an uppercase English letter
- •0 <= n <= 100
Examples
tasks = ["A","A","A","B","B","B"], n = 2
8
A possible sequence is A -> B -> idle -> A -> B -> idle -> A -> B.
tasks = ["A","A","A","B","B","B"], n = 0
6
With no cooling interval, tasks can be executed continuously without idles.
Need a Hint?
Edge Cases to Watch
- Empty input structures
- Single element inputs
- Large numerical bounds
Ready to Solve?
Open the problem in PyRun's browser-based Python editor. Your code runs fully offline — no server required.
Interview Insights & Variations
Complexity Analysis Breakdown
Why Time: Directly evaluates all possibilities.
Why Space: Uses standard local memory.
Why Time: Optimized paths reduce total operations.
Why Space: May trade memory for speed.
Optimized Solution Python Code
Optimized Solution Python Code
import heapq, collections
def least_interval_opt(tasks, n):
count = collections.Counter(tasks)
maxHeap = [-cnt for cnt in count.values()]
heapq.heapify(maxHeap)
time = 0
q = collections.deque()
while maxHeap or q:
time += 1
if maxHeap:
cnt = 1 + heapq.heappop(maxHeap)
if cnt: q.append([cnt, time + n])
if q and q[0][1] == time:
heapq.heappush(maxHeap, q.popleft()[0])
return timeBrute Force Code (Spoiler Guarded)
Brute Force Code (Spoiler Guarded)
def least_interval_brute(tasks, n):
import collections
counts = collections.Counter(tasks)
max_f = max(counts.values())
ans = (max_f - 1) * (n + 1)
for f in counts.values():
if f == max_f: ans += 1
return max(len(tasks), ans)Algorithm Pattern Checklist
When dealing with Heap / Priority Queue data patterns.
Core Prerequisites
Revision Key Notes
Common Mistakes & Pitfalls
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