AlgoScope

Bipartite Matching

AlgorithmadvancedNetwork Flow

Unit-capacity network from a source through left and right to a sink; each augmenting path is an alternating path.

Decision · step 2 of 8Maximum Flow: Bipartite matching by augmenting paths
sL1L2L3R1R2R3t

Augmenting path L1 > R1: L1 is free and so is R1, a direct match.

Open in the player →or start at step 2

What you will see

Two columns of vertices; an augmenting path flips matched and unmatched edges.

How maximum flow works →

Cost

BestO(E)
AverageO(V * E)
WorstO(V * E)
SpaceO(V + E)

Hopcroft-Karp achieves O(E sqrt(V)).

How you work with it here

play it through, step one change at a time, scrub to any step, run it on your own input.

Screen readers: Edges announce capacity, flow and residual; each step announces the augmenting path and bottleneck.

Reduced motion: Flow labels update with a crossfade; the augmenting path is emphasized statically.

Taught by the same lesson

Maximum Flow covers these too, in the same run.