TTRISK: Tensor train decomposition algorithm for risk averse optimization

Abstract

This article develops a new algorithm named TTRISK to solve high‐dimensional risk‐averse optimization problems governed by differential equations (ODEs and/or partial differential equations [PDEs]) under uncertainty. As an example, we focus on the so‐called Conditional Value at Risk (CVaR), but the approach is equally applicable to other coherent risk measures. Both the full and reduced space formulations are considered. The algorithm is based on low rank tensor approximations of random fields discretized using stochastic collocation. To avoid nonsmoothness of the objective function underpinning the CVaR, we propose an adaptive strategy to select the width parameter of the smoothed CVaR to balance the smoothing and tensor approximation errors. Moreover, unbiased Monte Carlo CVaR estimate can be computed by using the smoothed CVaR as a control variate. To accelerate the computations, we introduce an efficient preconditioner for the Karush–Kuhn–Tucker (KKT) system in the full space formulation.The numerical experiments demonstrate that the proposed method enables accurate CVaR optimization constrained by large‐scale discretized systems. In particular, the first example consists of an elliptic PDE with random coefficients as constraints. The second example is motivated by a realistic application to devise a lockdown plan for United Kingdom under COVID‐19. The results indicate that the risk‐averse framework is feasible with the tensor approximations under tens of random variables.

Adjoint-based Determination of Weaknesses in Structures

“…-Will these techniques work under uncertain measurements ? [2,3]. Furthermore, the steepest descent procedures may be improved by going to a quasi or full Newton solver.…”

Editorial: Tensor numerical methods and their application in scientific computing and data science

“…Antil et al 5 present a new algorithm for the solution of high‐dimensional risk‐averse optimization problems governed by partial differential equations (PDE) or ordinary differential equations is proposed. The algorithm is based on low rank tensor approximations of discretized random fields and an efficient preconditioner for the optimized system in the full space formulation.…”