Complex Alexandrov-Bakelman-Pucci estimate and its applications

TL;DR

This paper establishes a new complex ABP estimate involving the integral of the complex Hessian determinant, leading to sharper gradient bounds for complex Monge-Ampère equations using De Giorgi iteration.

Contribution

It introduces a novel complex ABP estimate that enhances classical bounds and applies it to derive sharp gradient estimates for complex Monge-Ampère equations.

Findings

New complex ABP estimate involving complex Hessian determinants

Improved gradient bounds for complex Monge-Ampère equations

Application of De Giorgi iteration to complex PDEs

Abstract

We prove an Alexandrov-Bakelman-Pucci type estimate, which involves the integral of the determinant of the complex Hessian over a certain subset. It improves the classical ABP estimate adapted (by inequality $2^{2n}|\det(u_{i\bar{j}})|^2\geq |\det(\nabla^2u)|$) to complex setting. We give an application of it to derive sharp gradient estimates for complex Monge-Amp\`ere equations. The approach is based on the De Giorgi iteration method developed by Guo-Phong-Tong for equations of complex Monge-Amp\`ere type.