A Subgradient Method for Free Material Design

TL;DR

This paper introduces a novel subgradient method for free material design, formulating the problem as a saddle-point optimization that avoids large matrix inversions, enabling efficient large-scale solutions with promising numerical results.

Contribution

It presents the first gradient-type method for free material optimization that avoids large matrix inversions and offers an efficient, scalable solution approach.

Findings

The method reduces computational complexity from O(N^3) to O(N^2) per iteration.

The problem is decomposed into small, parallelizable subproblems.

Numerical results demonstrate the effectiveness of the proposed approach.

Abstract

A small improvement in the structure of the material could save the manufactory a lot of money. The free material design can be formulated as an optimization problem. However, due to its large scale, second-order methods cannot solve the free material design problem in reasonable size. We formulate the free material optimization (FMO) problem into a saddle-point form in which the inverse of the stiffness matrix A(E) in the constraint is eliminated. The size of A(E) is generally large, denoted as N by N. This is the first formulation of FMO without A(E). We apply the primal-dual subgradient method [17] to solve the restricted saddle-point formula. This is the first gradient-type method for FMO. Each iteration of our algorithm takes a total of $O(N^2)$ foating-point operations and an auxiliary vector storage of size O(N), compared with formulations having the inverse of A(E) which requires $O(N^3)$ arithmetic operations and an auxiliary vector storage of size $O(N^2)$. To solve the problem, we developed a closed-form solution to a semidefinite least squares problem and an efficient parameter update scheme for the gradient method, which are included in the appendix. We also approximate a solution to the bounded Lagrangian dual problem. The problem is decomposed into small problems each only having an unknown of k by k (k = 3 or 6) matrix, and can be solved in parallel. The iteration bound of our algorithm is optimal for general subgradient scheme. Finally we present promising numerical results.