A Unified Framework for Generalized Moment Problems: a Novel Primal-Dual Approach

TL;DR

This paper introduces a unified primal-dual framework for generalized moment problems, enabling analytical or semi-analytical solutions and efficient algorithms for a broad class of moment constraints.

Contribution

It develops a novel primal-dual optimality condition that reduces infinite-dimensional problems to finite systems, applicable to various generalized moment problems.

Findings

Framework successfully derives analytical solutions for specific problems

Produces semi-analytical solutions for complex moment problems

Numerical experiments demonstrate the effectiveness of the algorithms

Abstract

Generalized moment problems optimize functional expectation over a class of distributions with generalized moment constraints, i.e., the function in the moment can be any measurable function. These problems have recently attracted growing interest due to their great flexibility in representing nonstandard moment constraints, such as geometry-mean constraints, entropy constraints, and exponential-type moment constraints. Despite the increasing research interest, analytical solutions are mostly missing for these problems, and researchers have to settle for nontight bounds or numerical approaches that are either suboptimal or only applicable to some special cases. In addition, the techniques used to develop closed-form solutions to the standard moment problems are tailored for specific problem structures. In this paper, we propose a framework that provides a unified treatment for any moment problem. The key ingredient of the framework is a novel primal-dual optimality condition. This optimality condition enables us to reduce the original infinite dimensional problem to a nonlinear equation system with a finite number of variables. In solving three specific moment problems, the framework demonstrates a clear path for identifying the analytical solution if one is available, otherwise, it produces semi-analytical solutions that lead to efficient numerical algorithms. Finally, through numerical experiments, we provide further evidence regarding the performance of the resulting algorithms by solving a moment problem and a distributionally robust newsvendor problem.