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Learn by Visualization
Visualize, Understand and Solve
Watch Kadane's algorithm slide over contiguous ranges in lockstep motion. Observe live as the running sum decides whether to grow or reset from scratch at each array index, while updating the record-breaking maximum sum in real-time.
Local vs. Global Maxima
Observe currentSum resets to zero when running sums become negative, avoiding sub-optimal ranges.
Contiguous Window Trace
Watch the active subarray boundaries dynamically stretch as positive integers expand the sum.
State Console Tracking
Inspect running variables like curSum and bestMax change line-by-line as code runs.
Step 1 / 41
Speed: 1.0x
Visualizing Subarray Sum Growth
Best Subarray Found (Overall Max)
Current Subarray (Running Total)
MaxSum-2
System Initialization
Step 1 of 41
Algorithm commentary
Step 1: Initialization. We set
maxSub to the first element (-2). The goal is to find a contiguous range with the highest sum.Variables
index-
value-
curSum0
maxSum-2
range[0, 0]
Kadane's Algorithm Strategy:
• Track running current sum (`curSum`) and overall maximum sum (`maxSub`)
• If `curSum` falls below 0 → reset it to 0 (since negative values hurt subsequent sums)
• Add the current element to `curSum` and update `maxSub` if it exceeds the record
• Time: O(n) · Space: O(1)
Stuck in thinking? Try our Thinkpad
Sometimes code isn't enough. Our built-in Thinkpad, powered by your favorite drawing tool Excalidraw, lets you draw, sketch, and take notes alongside complex algorithms before you start typing. It's the perfect tool for when you need to visualize logic that's hard to hold in your head.
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Perfect for dry-running tree and graph traversals
Save and reference your logic while you code
Thinkpad Canvas
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TELEMETRY
Case 1
nums = [-2,1,-3,4,-1,2,1,-5,4]Expected: 6Got: ?
Case 2
nums = [5,4,-1,7,8]Expected: 23Got: ?
Case 3
nums = [1]Expected: 1Got: ?
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