Inverse Multiobjective Optimization Through Online Learning

Abstract

We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions. In particular, these decisions might not be exact and possibly carry measurement noise or are generated with the bounded rationality of decision makers. In this paper, we propose a general online learning framework to deal with this learning problem using inverse multiobjective optimization. More precisely, we develop two online learning algorithms with implicit update rules which can handle noisy data. Numerical results show that both algorithms can learn the parameters with great accuracy and are robust to noise.

17 pages

References in corpus (5)

• Generalized Inverse Optimization through Online Learning
• Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox
• Learning Linear Programs from Optimal Decisions
• Inferring Parameters Through Inverse Multiobjective Optimization
• Wasserstein Distributionally Robust Inverse Multiobjective Optimization