Duality-Based Algorithm and Numerical Analysis for Optimal Insulation Problems on Non-Smooth Domains

TL;DR

This paper introduces a duality-based numerical framework for solving optimal insulation problems involving non-smooth, non-local minimization, providing error estimates and convergence analysis for discretized approximations.

Contribution

It develops a novel duality approach and error analysis for convex non-local non-smooth optimization problems, including a new primal reconstruction formula.

Findings

Optimal error decay rates achieved based on regularity.

Convergence of numerical approximations under minimal assumptions.

First primal reconstruction formula for this class of problems.

Abstract

This article develops a numerical approximation of a convex non-local and non-smooth minimization problem. The physical problem involves determining the optimal distribution, given by $h\colon \Gamma_I\to [0,+\infty)$, of a given amount $m\in \mathbb{N}$ of insulating material attached to a boundary part $\Gamma_I\subseteq \partial\Omega$ of a thermally conducting body $\Omega \subseteq \mathbb{R}^d$, $d \in \mathbb{N}$, subject to conductive heat transfer. To tackle the non-local and non-smooth character of the problem, the article introduces a (Fenchel) duality framework: (a) At the continuous level, using (Fenchel) duality relations, we derive an a posteriori error identity that can handle arbitrary admissible approximations of the primal and dual formulations of the convex non-local and non-smooth minimization problem; (b) At the discrete level, using discrete (Fenchel) duality relations, we derive an a priori error identity that applies to a Crouzeix--Raviart discretization of the primal formulation and a Raviart--Thomas discretization of the dual formulation. The proposed framework leads to error decay rates that are optimal with respect to the specific regularity of a minimizer. In addition, we prove convergence of the numerical approximation under minimal regularity assumptions. Since the discrete dual formulation can be written as a quadratic program, it is solved using a primal-dual active set strategy interpreted as semismooth Newton method. A solution of the discrete primal formulation is reconstructed from the solution of the discrete dual formulation by means of an inverse generalized Marini formula. This is the first such formula for this class of convex non-local and non-smooth minimization problems.