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10.31.21 Exponential and normalized equations6720AA8AAF70

The smoothing argument solves both equations for measures with finite $A_\theta $. A bounded carrier representation reduces an arbitrary finite non-pluripolar measure to increasing bounded densities, whose solutions have measures dominated by one fixed finite measure; theorem 10.31.5 then passes to the limit.

Theorem 10.31.22.AC1DBB01A890 For every finite non-pluripolar measure $\mu $ with $\mu (X)>0$, the following assertions hold.

  1. For every $\lambda >0$ there is a unique $u\in \mathcal E_ V$ such that

    \[ \mathrm{MA}_\theta (u)=\mathrm{e}^{\lambda u}\mu . \]
  2. There are $v\in \mathcal E_ V$ with $\sup _ Xv=0$ and a unique scalar $c>0$ such that

    \[ \mathrm{MA}_\theta (v)=c\mu . \]

Proof. First suppose that $\mu $ is a probability measure with $A_\theta (\mu )<\infty $, and let $\mu _ j$ be its smooth probability approximations. The theorems for the minimal ordinary type, theorem 15.16.2, theorem 15.16.7, apply to their smooth, nonzero densities.

For the normalized equation, they give $v_ j\sim V$ with $\sup _ Xv_ j=0$ and $\mu _{v_ j}=c_ j\mu _ j$. Since $\mu _ j$ is a probability measure, the mass bounds $\ell _ V\leq \mu _{v_ j}(X)\leq M_ V$ give $\ell _ V\leq c_ j\leq M_ V$. After extraction, $c_ j\to c>0$ and $v_ j\to v$ in $L^1(X)$. The preceding proposition applies, so $v\in \mathcal E_ V$ and $\mu _{v_ j}\rightharpoonup \mu _ v$. The right-hand sides converge weakly to $c\mu $, which proves the equation.

For the positive-exponent equation, take $u_ j\sim V$ satisfying $\mu _{u_ j}=\mathrm{e}^{\lambda u_ j}\mu _ j$, and write $a_ j=\sup _ Xu_ j$ and $v_ j=u_ j-a_ j$. The mass bounds and Jensen's inequality give

\[ \ell _ V\leq \mathrm{e}^{\lambda a_ j},\qquad M_ V\geq \mathrm{e}^{\lambda a_ j}\int _ X\mathrm{e}^{\lambda v_ j}\, \mathrm{d}\mu _ j \geq \mathrm{e}^{\lambda a_ j-\lambda A_\theta (\mu _ j)}, \]

and $\sup _ jA_\theta (\mu _ j)<\infty $ by lemma 5.44.12. Thus $a_ j$ is bounded above and below. After extraction, $a_ j\to a$, $v_ j\to v$ in $L^1(X)$, and $\mu _{v_ j}\leq C\mu _ j$. The preceding proposition gives $v\in \mathcal E_ V$, convergence in $L^1(\mu )$ and weak convergence of the Monge–Ampère measures. Set $u=v+a$. By the smoothing lemma, the right-hand sides converge weakly to $\mathrm{e}^{\lambda u}\mu $. This proves the equation, and $u\in \mathcal E_ V$.

The same conclusions hold for every nonzero finite measure whose normalization to a probability measure has finite $A_\theta $, since scaling the measure only changes the positive scalar, or translates the solution of the exponential equation by a constant.

Now let $\mu $ be an arbitrary finite non-pluripolar measure of positive mass. The bounded-carrier decomposition lemma 15.24.4, applied to $\omega $, writes

\[ \mu =f\nu ,\qquad \nu =\mathrm{MA}_\omega (\psi ),\qquad \psi \in \mathrm{PSH}(X,\omega )\cap L^\infty (X),\qquad 0\leq f\in L^1(\nu ). \]

For $\mu _ k=\min (f,k)\nu $, we have $\mu _ k(X)>0$. By lemma 5.44.10, the normalization of $\mu _ k$ has finite $A_\theta $ for each fixed $k$.

For the normalized equation, the preceding case gives $v_ k\in \mathcal E_ V$ with $\sup _ Xv_ k=0$ and $\mu _{v_ k}=c_ k\mu _ k$, where

\[ \frac{\ell _ V}{\mu (X)}\leq c_ k\leq \frac{M_ V}{\mu _1(X)}. \]

After extraction, $c_ k\to c>0$ and $v_ k$ converges in $L^1$ to some $v\not\equiv -\infty $. All the measures $\mu _{v_ k}$ are bounded by the fixed finite non-pluripolar measure $M_ V\mu /\mu _1(X)$. The fixed-dominator theorem theorem 10.31.5 gives $v\in \mathcal E_ V$ and weak convergence to $\mu _ v$. Since $c_ k\mu _ k\to c\mu $ in total variation, $\mu _ v=c\mu $.

For the positive-exponent equation, let $u_ k\in \mathcal E_ V$ solve $\mu _{u_ k}=\mathrm{e}^{\lambda u_ k}\mu _ k$. Since $\mu _ k$ increases with $k$, exponential order (corollary 13.19.3) gives $u_{k+1}\leq u_ k$. Also

\[ \ell _ V\leq \mu _{u_ k}(X)\leq \mathrm{e}^{\lambda \sup _ Xu_ k}\mu (X), \qquad \mu _{u_ k}\leq \mathrm{e}^{\lambda \sup _ Xu_1}\mu . \]

The lower bound on the suprema shows that the decreasing limit $u$ is not identically $-\infty $. The same fixed-dominator theorem puts $u$ in $\mathcal E_ V$ and gives weak convergence of the left-hand sides. Dominated convergence with respect to $\nu $, with the integrable bound $\mathrm{e}^{\lambda \sup _ Xu_1}f$, gives convergence of the right-hand sides to $\mathrm{e}^{\lambda u}\mu $ in total variation.

Finally, corollary 13.19.3 with reference $V$, applied in both directions, gives uniqueness of the exponential solution and of the positive scalar. $\square$

Positive lower mass of a Hermitian form gives positive lower volume to the minimal reference of every smooth big background.

Corollary 10.31.23.C8ACE6D27FAB Suppose that $X$ has the bounded mass property and $v_-(\omega )>0$. Then $\underline{\operatorname {vol}}_\theta (V_\theta )>0$ for every smooth big real $(1,1)$-form $\theta $ on $X$; hence the preceding theorem applies to every such $\theta $.

Proof. Choose $\rho \in \mathrm{PSH}(X,\theta )$ with $\theta _\rho \geq a\omega $ for some $a>0$. Then $\rho \in \mathrm{PSH}(X,\theta -a\omega )$. Volume addition (proposition 13.17.2), followed by monotonicity in the singularities (theorem 13.17.1), gives

\[ 0<a^ n v_-(\omega ) =\underline{\operatorname {vol}}_{a\omega }(0) \leq \underline{\operatorname {vol}}_\theta (\rho ) \leq \underline{\operatorname {vol}}_\theta (V_\theta ). \]

In the addition step the big background is $a\omega $, and the added positive current is $(\theta -a\omega )_\rho $. Bounded mass gives finiteness of the upper volume. $\square$

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