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The assignment problem chooses a one-to-one pairing of jobs and agents minimizing their total cost. Its minimum-cost flow formulation puts unit supplies on job vertices and unit demands on agent vertices of a bipartite graph. Integral feasible flows are perfect matchings.

A closed directed path whose vertices are distinct except for the common start and endpoint is a directed cycle. In a residual network, augmenting around a directed cycle preserves flow balance. A negative total cost on such a cycle gives an improving displacement for a minimum-cost flow.

For a connected capacitated minimum-cost flow network, a spanning tree supplies basic flow variables. Every non-tree edge is fixed at its lower or upper bound. Flow balance determines the tree flows; these must also obey capacity constraints. An entering edge creates one cycle, and adjusting flow on that cycle preserves balance until a bound is hit and an edge leaves the tree.

For a minimum-cost flow with bounds $0\le x_{ij}\le m_{ij}$, the capacitated flow optimality conditions consist of primal feasibility and network dual potentials whose network reduced costs obeyFor a zero-capacity edge both bounds coincide and no sign restriction is needed; all capacities here are positive. These are the bound form of complementary slackness. To derive necessity directly, construct the residual network: a possible increase has original cost $c_{ij}$ and a possible decrease has reverse cost $-c_{ij}$. An optimal flow cannot admit a negative-cost directed cycle, since a positive step around it preserves flow balance and lowers cost. If there is no such cycle, connect a new source by zero-cost edges to every vertex and let $d_i$ be shortest-path distances. They exist because no negative cycle is reachable. For every residual edge $i\to j$, $d_j\le d_i+c_{ij}^{\mathrm{res}}$. Taking ${\pi }_i=-d_i$ makes every residual network reduced cost nonnegative, exactly the displayed sign conditions: both directions exist for an interior edge.

Conversely, for any other feasible flow $y$, equal flow balances giveEach summand is nonnegative at a lower or upper bound, and zero at an interior edge. Hence the sign conditions suffice for optimality as well.

For the final flow, use $\pi =(0,-7,-10,-11,-12)$ in the order $(S,A,B,C,T)$. All four tree edges have zero network reduced cost, while $SB,AB,BC$ have costs $-4,2,1$. More explicitly, every feasible flow satisfiesOur flow attains equality. This gives a direct cost certificate of 220. The strict signs also force $y_{SB}=20$, $y_{AB}=y_{BC}=0$ at any optimum; flow balance then fixes all remaining edges, so this flow is unique.

Use a directed bipartite graph with one vertex for each job and one for each agent. Job vertices have supply $1$ and agent vertices demand $1$. An arc from job $i$ to agent $j$ has cost $c_{ij}$ and nonnegative flow $x_{ij}$; an upper bound $1$ may be added, but is already implied by the balances. The minimum-cost flow formulation of the assignment problem isInteger supplies and demands give an integral optimum by integrality of the transportation problem. Its $n$ positive arcs form a perfect matching, hence give one job per agent.

A network simplex algorithm basis for this connected $2n$-vertex network has $2n-1$ tree arcs, whereas an integral assignment has only $n$ positive flows. For $n>1$, at least $n-1$ basic tree arcs therefore carry zero flow. This unavoidable degeneracy in linear programming can cause zero-step pivots, extra bookkeeping and cycling unless an appropriate pivot rule is used. The Hungarian algorithm exploits the assignment structure directly rather than maintaining many zero-flow basic arcs.

Suppose the assignment dual potentials satisfy ${\lambda }_i-{\mu }_j\le c_{ij}$. For every feasible assignment $y$,If the selected assignment $x$ uses only arcs with ${\lambda }_i-{\mu }_j=c_{ij}$, its cost equals this lower bound. Weak duality and complementary slackness certify optimality. The PDF correctly denotes the feasible assignment by $x_{ij}$; the TeX aid mistakenly replaces it with ${\lambda }_{ij}$.

The Hungarian algorithm maintains feasible assignment dual potentials and a matching in a graph of zero reduced cost edges, where $r_{ij}=c_{ij}-{\lambda }_i+{\mu }_j\ge 0$. Search for an augmenting path in a matching in this equality graph. If there is none, let $S$ be the reachable jobs and $T$ the reachable agents in the alternating search from unmatched jobs. Every agent in $T$ is matched to a job in $S$. SetReduced costs from $S$ to its complement decrease by $\Delta$, those from outside $S$ into $T$ increase, and the remaining ones do not change. Thus feasibility and current matching equalities are preserved, while at least one new equality edge appears. Continue until an augmenting path in a matching increases the matching size. A perfect matching in the equality graph then has the same value as the dual linear program and is optimal.

For the example, start with the row minima $\lambda =(10,50,50)$ and $\mu =(0,0,0)$. The reduced cost matrix isMatch job $1$ to agent $1$. Searching from both unmatched jobs gives $S=\{1,2,3\}$ and $T=\{1\}$; the minimum cost to an agent outside $T$ is $\Delta =10$. Update to $\lambda =(20,60,60)$ and $\mu =(10,0,0)$:The new equality edge $(2,3)$ augments the matching. From unmatched job $3$, the next alternating search reaches agent $1$ and its matched job $1$, giving $S=\{1,3\}$ and $T=\{1\}$. Now $\Delta =15$. The updated feasible assignment dual potentials and reduced costs areThe augmenting path in a matching $J_3\to A_1\to J_1\to A_2$ uses job $3$ to agent $1$, the reversed matched edge to job $1$, then the new edge to agent $2$. It gives the optimal assignmentThe dual linear program value is $35+60+75-25=145$, an independent optimality certificate.

The minimum-cost flow problem chooses, for each directed edge $(i,j)\in E$, a flow $x_{ij}$ and solvessubject toFeasibility requires ${\sum }_i{b}_i=0$.

Set $y_{ij}=x_{ij}-M_{ij}$. Thenand its balance vector isThe objective becomes $\sum C_{ij}{y}_{ij}$ plus the constant $\sum C_{ij}{M}_{ij}$. Translation by $M$ is a bijection between feasible flows and preserves their ordering by cost, so the transformed zero-lower-bound problem is equivalent.

An uncapacitated minimum-cost flow has nonnegative edge flows without finite upper capacities. The flow balances prescribe net supply at each vertex. For the oriented incidence matrix convention with tail $+1$ and head $-1$, vertex network dual potentials have network reduced costs $c_{ij}-{\pi }_i+{\pi }_j$. Nonnegative network reduced costs and complementary slackness certify a feasible optimum. A feasible negative-cost directed cycle makes the problem unbounded below.