Deep Inverse Optimization

TL;DR

Deep inverse optimization uses deep learning techniques to infer parameters of an optimization process from observations by unrolling algorithms and applying backpropagation, enabling flexible and efficient parameter learning.

Contribution

The paper introduces a novel deep learning framework for inverse optimization that unrolls optimization algorithms and applies backpropagation to learn parameters from data.

Findings

Successfully learns cost vector and constraints from observations

Can handle both parametric and non-parametric linear programs

Leverages deep learning algorithms for inverse optimization

Abstract

Given a set of observations generated by an optimization process, the goal of inverse optimization is to determine likely parameters of that process. We cast inverse optimization as a form of deep learning. Our method, called deep inverse optimization, is to unroll an iterative optimization process and then use backpropagation to learn parameters that generate the observations. We demonstrate that by backpropagating through the interior point algorithm we can learn the coefficients determining the cost vector and the constraints, independently or jointly, for both non-parametric and parametric linear programs, starting from one or multiple observations. With this approach, inverse optimization can leverage concepts and algorithms from deep learning.