Reproducing kernel method for PDE constrained optimization

TL;DR

This paper introduces a reproducing kernel Hilbert space approach to numerically solve optimization problems constrained by partial differential equations, offering a potentially efficient computational method.

Contribution

The paper develops a novel reproducing kernel Hilbert space method specifically designed for PDE constrained optimization problems.

Findings

Demonstrates the method's effectiveness through numerical examples

Achieves accurate solutions with fewer computational resources

Provides a new framework for PDE constrained optimization

Abstract

This paper presents reproducing kernel Hilbert spaces method to obtain the numerical solution for partial differential equation constrained optimization problem.