We consider stochastic zero-order optimization problems, which arise in settings from simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients of a stochastic function using finite differences within a common random number framework. We employ modified versions of a norm test and an inner product quasi-Newton test … Read more
A common assumption in the shop scheduling literature is that the processing order of the operations of each job is sequential; however, in practice there can be multiple connections and finish-to-start dependencies among the operations of each job. This paper studies flexible job shop scheduling problems with arbitrary precedence graphs. Rigorous mixed integer and constraint … Read more
We present a strikingly simple proof that two rules are sufficient to automate gradient descent: 1) don’t increase the stepsize too fast and 2) don’t overstep the local curvature. No need for functional values, no line search, no information about the function except for the gradients. By following these rules, you get a method adaptive … Read more
Predictive analytics, empowered by machine learning, is usually followed by decision-making problems in prescriptive analytics. We extend the above sequential prediction-optimization paradigm to a coupled scheme such that the prediction model can guide the decision problem to produce coordinated decisions yielding higher levels of performance. Speci fically, for stochastic programming (SP) models with latently decision-dependent uncertainty, … Read more
This paper proposes a cluster-aware supervised learning (CluSL) framework, which integrates the clustering analysis with supervised learning (SL). The objective of CluSL is to simultaneously find the best clusters of the data points and minimize the sum of loss functions within each cluster. This framework has many potential applications in healthcare, operations management, manufacturing, and … Read more
The paper presents a fully adaptive proximal extrapolated gradient method for monotone variational inequalities. The proposed method uses fully non-monotonic and adaptive step sizes, that are computed using two previous iterates as an approximation of the locally Lipschitz constant without running a linesearch. Thus, it has almost the same low computational cost as classic proximal … Read more
Techniques based on sharing data and computation among queries have been an active research topic in database systems. While work in this area developed algorithms and systems that are shown to be effective, there is a lack of rigorous modeling and theoretical study for query processing and optimization. Query batching in database systems has strong … Read more
Smart homes allow the optimization of energy usage so that households can reduce electricity bills, or even make profits. By 2020, 20% of all households in Europe and 35% in North America will be expected to become smart homes. Although smart homes seem to be the future for homes, many customers have the perception that … Read more
The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications. In this paper, we propose a non-parametric approximation of the likelihood that identifies a probability measure which lies in the neighborhood of the nominal measure and that maximizes the probability of observing … Read more
The minimum cut problem, MC, and the special case of the vertex separator problem, consists in partitioning the set of nodes of a graph G into k subsets of given sizes in order to minimize the number of edges cut after removing the k-th set. Previous work on this topic uses eigenvalue, semidefinite programming, SDP, … Read more