We present a mathematical and computational framework for the problem of learning a dynamical system from noisy observations of a few trajectories and subject to side information. Side information is any knowledge we might have about the dynamical system we would like to learn besides trajectory data. It is typically inferred from domain-specific knowledge or … Read more
Mine planning optimization aims at maximizing the profit obtained from extracting valuable ore. Beyond its theoretical complexity (the open-pit mining problem with capacity constraints reduces to a knapsack problem with precedence constraints, which is NP-hard), practical instances of the problem usually involve a large to very large number of decision variables, typically of the order … Read more
We consider a system with an evolving state that can be stopped at any time by a decision maker (DM), yielding a state-dependent reward. The DM does not observe the state except for a limited number of monitoring times, which he must choose, in conjunction with a suitable stopping policy, to maximize his reward. Dealing … Read more