dedicated visualizer
Dijkstra's Algorithm
Finds the shortest path from the source to every reachable node when all edge weights are non-negative. This page keeps the runner, chart, and controls focused on a single algorithm so the walkthrough feels calmer than the overview page.
session controls
Compare this algorithm against a related one, turn on quiz mode, or keep the current state in a shareable URL.
current shareable URL
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open catalogscenario presets
Load a focused input that reveals a specific behavior quickly instead of hand-editing every value first.
graph controls
Graph algorithms reuse the same learning graph so you can compare traversal and shortest-path behavior side by side.
graph note
BFS and DFS emphasize traversal order. Dijkstra adds weighted relaxations and a cost-aware final route.
chart + counters
The visualization and the live counters stay together so each step is easier to read.
current action · initialize distances
current action
initialize distances
visited
0
final 7
frontier
1
final 0
relaxations
0
final 7
path cost
0
final 8
steps
1 / 25
current node
none
run summary
Finished in 25 steps. G has the shortest finalized distance, so Dijkstra can stop.
visited
7
relaxations
7
path cost
8
frontier
0
steps
25
final route
A → C → D → E → G
cost 8
current explanation
Initialize A with distance 0 and every other node with infinity.
simple explanation
At the start, only the source has a known distance.
pseudocode
complexity card
best
O((V + E) log V)
average
O((V + E) log V)
worst
O((V + E) log V)
space
O(V)
algorithm notes
intuition
Dijkstra greedily finalizes the nearest unfinished node because non-negative weights make that choice safe.
tradeoffs
- Great for non-negative weighted shortest paths.
- Not valid when negative-weight edges exist.
- Priority queues improve performance on larger graphs.
when to use it
Use for shortest-path problems on weighted graphs with non-negative edge costs.
interview tips
- Explain why finalized nodes never need to be revisited with non-negative weights.
- Be ready to contrast Dijkstra with BFS and Bellman-Ford.
what I learned building this
typed definitions
One algorithm schema now drives the catalog, counters, pseudocode, notes, and visual modes, which keeps the UI consistent as the lab grows.
replay over mutation
Precomputed steps made it much easier to synchronize explanations, metrics, quiz prompts, and scrubber playback without hidden state drifting out of sync.
portfolio framing
Shareable URL state, compare mode, and responsive layouts mattered as much as the algorithm logic because this page needs to teach clearly and still feel polished as a product.
more in this lane
Want a different take on the same problem family? These stay in the same category but change the strategy.