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In multiplicative resource-price dynamics, a zero price remains zero even if demand exceeds supply there. For one route using two resources, unit weight and supplies $q_j(u)=u$, both $(0,1)$ and $(1,0)$ are equilibria, while the unique positive equilibrium is $(1/\sqrt{2},1/\sqrt{2})$. Thus uniqueness on the positive price domain cannot be extended to every nonnegative price trajectory without an extra boundary rule.

For multiplicative resource-price dynamics with positive capacities, nonempty routes and strictly positive initial prices, full row rank of the link-route incidence matrix makesstrictly concave. There is a unique optimizing price vector, possibly on the boundary, and the relative-entropy Lyapunov function for resource prices proves convergence to it. Row-rank deficiency still gives unique optimal route prices and rates, but resource prices may depend on the initial state. Full column rank alone and unrestricted zero initial prices do not yield the same uniqueness theorem.

For the increasing-supply resource-price dynamics, assume a finite network, positive weights $w_r$, nonempty routes, nonnegative prices, and initially positive prices on every used resource. Resource $j$ announces price ${\mu }_j$, and a route sees total price $s_r={\sum }_{j\in r}{\mu }_j$. Maximizing $w_r\log x-s_{r}x$ over $x>0$ gives $x_r=w_r/s_r$: this is the route's weighted proportional fairness demand. The supply $q_j({\mu }_j)$ increases with its price, and the multiplicative resource-price dynamics raise a positive price when total demand exceeds supply and lower it otherwise.

The boundary equilibria of multiplicative resource prices show why the positivity qualification is necessary. With two resources, one route using both, $w=1$, ${\kappa }_1={\kappa }_2=1$ and $q_j(u)=u$, the prices $(0,1)$ and $(1,0)$ are distinct equilibria with well-defined route price one. The positive equilibrium is $(1/\sqrt{2},1/\sqrt{2})$. A zero coordinate stays zero because it multiplies its own derivative. Thus the printed claim about all trajectories is false if these boundary initial conditions are admitted. We now prove the intended convergence for positive initial prices; unused resources may start at zero.

For positive-price convergence for increasing resource supplies, define $Q_j(u)={\int }_0^u{q}_j(\eta )d\eta$. Strict increase of $q_j$ makes $Q_j$ strictly convex, even without differentiability of $q_j$. On the convex domain $\mu \ge 0$ with every $s_r>0$, the supplied function $V={\sum }_r{w}_r\log s_r-{\sum }_j{Q}_j({\mu }_j)$ is strictly concave, since each logarithmic term is concave and the negative integral terms give strictness in every changed coordinate. Its gradient isIt is also coercive toward large prices: for fixed $a>0$, $Q_j(u)\ge q_j(a)(u-a)$ when $u\ge a$, while the positive logarithmic terms grow only logarithmically in the largest coordinate. If any route price tends to zero with the other prices bounded, $V\to -\infty$. Consequently every nonempty superlevel set is compact and bounded away from zero route prices. $V$ attains a unique maximum ${\mu }^{\ast }$.

For a used resource, the derivative at a zero coordinate is ${\sum }_{r:j\in r}{w}_r/s_r>0$, since $q_j(0)=0$. Such a coordinate cannot vanish at the maximum. An unused resource has its unique maximizing coordinate at zero. Thus ${\mu }^{\ast }$ is positive on used resources and satisfies demand equal to supply there. It is the unique equilibrium in that positive domain.

Along any trajectory,The initial superlevel set traps the trajectory and supplies a global upper bound $M$ on every price, and a lower bound on every route price. On any finite time interval $g_j$ is bounded, so ${\mu }_j(t)={\mu }_j(0)\exp ({\kappa }_j{\int }_0^t{g}_j(\mu (u))du)$ stays positive if initially positive. The bounded trajectory therefore exists for all positive times.

More strongly, each used resource has a uniform positive demand boundChoose ${\delta }_j>0$ with $q_j({\delta }_j)<c_j/2$. While $0<{\mu }_j\le {\delta }_j$, its derivative is positive, so ${\mu }_j(t)\ge \min ({\mu }_j(0),{\delta }_j)>0$. Unused prices satisfy ${\dot{\mu }}_j=-{\kappa }_j{\mu }_j{q}_j({\mu }_j)$ and converge to zero.

The nonnegative function $dV/dt$ has finite integral, since $V$ is increasing and bounded above. It is uniformly continuous along the trajectory: on the compact trapped set, the vector field is bounded, the trajectory is Lipschitz, and ${\sum }_j{\kappa }_j{\mu }_j{g}_j^2$ is a continuous, hence uniformly continuous function of $\mu$. The criterion uniformly continuous integrable functions vanish at infinity applies here: otherwise separated intervals around positive peaks would give an infinite integral. Therefore $dV/dt\to 0$. Since used prices stay bounded away from zero, $g_j(\mu (t))\to 0$ on every used resource. Every limit point is thus the unique maximizer ${\mu }^{\ast }$, with zero prices on unused resources. Compactness now implies $\boxed{\mu (t)\to {\mu }^{\ast }}$. Only continuity and strict increase of the supply functions were used; differentiability or an unbounded supply was not required. The boundary counterexample remains a necessary qualification of the literal source assertion.

Write $p_r=(A^T\mu {)}_r$ for a route congestion price and $y_j=(Ax{)}_j$ for resource demand. A route with weighted logarithmic utility $w_r\log x_r$ chooses $x_r=w_r/p_r$, since its marginal utility equals its route congestion price. Each resource raises its price when $y_j>C_j$ and lowers it when $y_j<C_j$; ${\kappa }_j$ controls the speed. This is a multiplicative resource-price dynamics model of supply-demand feedback.

For a well-defined finite network, assume positive capacities, nonempty routes, and strictly positive initial resource congestion prices. The relevant rank condition is full row rank, $\mathrm{rank}A=|J|$. Zero initial prices are invariant under the printed differential equation, so the unrestricted phrase “all trajectories” needs a qualification; a concrete counterexample is given below.

Define $g(\mu )=Ax(\mu )-C$. On the domain $\mu \ge 0$ with $p_r>0$, direct differentiation givesThus $V$ is a concave function, and it is a strictly concave function when $A$ has full row rank. It has a maximum: as $\Vert \mu {\Vert }_1\to \infty$, its negative linear capacity term dominates its logarithmic terms, while on bounded sets $V\to -\infty$ if any route congestion price approaches zero. A maximizing price vector ${\mu }^{\ast }$ therefore exists with every $p_r^{\ast }>0$, though some individual resource congestion prices may be zero. The Karush-Kuhn-Tucker conditions areThey express feasibility and complementary slackness: positively priced resources are fully used, and an underused resource has zero price. Full row rank gives uniqueness of this maximizing price vector.

To prove convergence, rather than merely monotonicity of $V$, use the relative-entropy Lyapunov function for resource priceswhere a summand with ${\mu }_j^{\ast }=0$ means ${\mu }_j/{\kappa }_j$. Every summand is nonnegative and vanishes only at its reference price. Along a positive-price trajectory,The last equality follows by substituting $x_r=w_r/p_r$, and complementary slackness removes ${\mu }^{\ast T}g({\mu }^{\ast })$.

A sublevel set of $L$ bounds every price from above and bounds every coordinate with ${\mu }_j^{\ast }>0$ away from zero. Each route contains at least one such coordinate, because $p_r^{\ast }>0$. All route congestion prices consequently remain bounded away from zero, so the vector field is regular on the resulting compact set. Positive coordinates cannot hit zero in finite time, since their equations have the form ${\dot{\mu }}_j={\mu }_j$ times a bounded function. These bounds also give global existence.

The LaSalle invariance principle now applies. Equality in $\dot{L}\le 0$ requires $A^T\mu =A^T{\mu }^{\ast }$ and ${\mu }_j{g}_j({\mu }^{\ast })=0$. Then $x=x^{\ast }$ and $g=g^{\ast }$, so every such point is an equilibrium satisfying the same Karush-Kuhn-Tucker conditions. If $A$ has full row rank, $A^T$ is injective, making this set the singleton ${\mu }^{\ast }$. Thusfor every strictly positive initial price vector. This proves full-row-rank convergence of multiplicative resource prices.

If $A$ is row-rank deficient, all maximizing price vectors still have the same route-price vector: otherwise strict concavity of ${\sum }_r{w}_r\log p_r$ makes their midpoint improve $V$. Consequently the limiting rates are unique, while individual prices may not be. Positive-price trajectories actually converge to a single point in that possibly non-singleton equilibrium set. To see this, choose any subsequential limit $\bar{\mu }$ in it, and form the same $L$ with reference $\bar{\mu }$. It is nonincreasing, and along the chosen subsequence it tends to zero. Hence it tends to zero on the whole trajectory, forcing $\mu (t)\to \bar{\mu }$.

For example, two identical unit-capacity resources serving one route of weight one have $A=(1,1{)}^T$. With ${\kappa }_1={\kappa }_2=1$, $p={\mu }_1+{\mu }_2$ obeys $\dot{p}=1-p$, while ${\mu }_1/{\mu }_2$ is constant. The equilibrium prices can be any nonnegative pair with sum one, and positive initial values select the limiting split; the route rate always tends to one. Rank deficiency can, but need not, cause price nonuniqueness. This example also shows why full column rank alone is insufficient.

Finally, the zero-price qualification is essential even for an invertible matrix. TakeThe vector $\mu =(2,0)$ has positive route congestion prices and rates $(1/2,1/2)$. Resource one is balanced, but resource two is overloaded; its zero price keeps its derivative zero. This is a boundary equilibrium of multiplicative price dynamics, distinct from the optimizing equilibriumTherefore full row rank gives the unique optimizing equilibrium and convergence from strictly positive prices; it does not give uniqueness of every equilibrium on the nonnegative orthant.

For a complementary-slackness optimum ${\mu }^{\ast }$, defineinterpreting a zero-reference summand as ${\mu }_j/{\kappa }_j$. This is a nonnegative generalized reverse-relative-entropy expression for positive resource prices. Along multiplicative resource-price dynamics, with $p=A^T\mu$ and $g=Ax-C$,Its sublevel bounds keep all route prices away from zero and permit the LaSalle invariance principle, including optima with some zero individual prices.

A resource congestion price signals scarcity at a capacity-constrained link. Under multiplicative resource-price dynamics, its increase indicates excess demand and its decrease indicates spare capacity. A route pays the sum of these prices, its route congestion price. A zero resource congestion price at an optimum satisfies complementary slackness with possible spare capacity.

The total price seen by a route is the sum of resource congestion prices on its links. With weighted logarithmic utility $w_r\log x_r$, maximizing utility minus linear expenditure gives the demand $x_r=w_r/p_r$. This relation links user decisions to multiplicative resource-price dynamics.