2016 INFORMS Annual Meeting Program

MD18

INFORMS Nashville – 2016

MD18 106A-MCC Statistical Learning under a Modern Optimization Lens Sponsored: Optimization, Integer and Discrete Optimization Sponsored Session Chair: Dimitris Bertsimas, Operations Research Center, MIT, MIT, E40-111, Cambridge, MA, 02139, United States, dbertsim@mit.edu Co-Chair: Dimitris Bertsimas, Operations Research Center, MIT, MIT, E40-111, Cambridge, MA, 02139, United States, dbertsim@mit.edu 1 - Missing Data Imputation Via A Modern Optimization Lens We propose an optimization framework for missing data imputation. The formulation imputes missing values to minimize the total distance to the K- nearest neighbors of each data point. Because the problem is non-convex, we derive a fast first-order method opt.impute to find high-quality solutions. On a sample of 64 data sets with 25% hidden values, opt.impute produces the best overall imputation in 40 data sets benchmarked against state-of-the-art methods. Further, the relative performance of opt.impute improves as the percentage of missing data increases, and for problems with the highest percentage of missing data, the mean absolute deviation from true values is lowered by 2.4%. 2 - Multi-target Tracking Via Mixed Integer Optimization Shimrit Shtern, Operations Research Center, MIT, sshtern@mit.edu, Zachary C. Saunders The Multi-target tracking problem combines the problems of assigning sensor detections to targets and estimating the target trajectory. Inaccuracies in the detection process make this a difficult problem. The literature is abundant with probability based models, but few optimization based models have been suggested, which mostly focus on model accuracy rather than interoperability or solvability. In our work we develop a simple and interpretable mixed integer optimization model, which is warm started by an appropriate heuristic, and yields high quality solutions in a reasonable time. 3 - Optimal Sparse Inverse Covariance Estimation Jourdain Bernard Lamperski, Operations Research Center, MIT, jourdain@mit.edu We consider the maximum likelihood estimation of sparse inverse covariance matrices. We formulate the estimation problem as a nonlinear MIP and develop a novel decomposition approach that solves the formulation to optimality. Using a variety of datasets, we demonstrate that the approach scales to problem sizes of interest and yields sparser solutions than existing methods, while maintaining desirable statistical properties. 4 - Optimal Low Rank Factor Analysis Martin S. Copenhaver, Operations Research Center, MIT, Cambridge, MA, United States, mcopen@mit.edu, Dimitris Bertsimas, Rahul Mazumder Factor Analysis (FA) is a technique that is widely used to obtain a parsimonious representation of correlation structure among a set of variables in terms of a smaller number of common hidden factors. In this talk we revisit the classical rank-constrained FA problem and propose a flexible family of exact, smooth reformulations for this task. By coupling state-of-the-art techniques from nonlinear optimization with methods from discrete and global optimization, we show that our approach often finds certifiably optimal solutions and significantly outperforms existing publicly available methods across a variety of real and synthetic examples. MD19 106B-MCC Networks and Geography in Optimization Sponsored: Computing Sponsored Session Chair: John Gunnar Carlsson, University of Southern California, University of Southern California, Los Angeles, CA United States, jcarlsso@usc.edu 1 - A Decomposition Heuristic For The One-warehouse Multi-retailer Problem With Batch Costs Alejandro Toriello, Georgia Institute of Technology, Atlanta, GA, United States, atoriello@isye.gatech.edu, Weihong Hu We consider a one-warehouse, multi-retailer system with deterministic dynamic demand over a discrete finite horizon, where orders placed by any facility incur batched fixed costs. We propose a decomposition heuristic that solves the warehouse’s and retailers’ problems separately and then judiciously converts the Colin Pawlowski, Massachusetts Institute of Technology, Cambridge, MA, United States, cpawlows@mit.edu, Dimitris Bertsimas, Ying Zhuo

separate solutions into a globally feasible solution with worst-case approximation guarantee of 2; this improves the previously best-known ratio of 3.6. Time permitting, we will discuss the extension to concave batch costs. 2 - Bounds For The Euclidean Generalized Travelling Salesman Problem John Gunnar Carlsson, University of Southern California, jcarlsso@usc.edu We study the Euclidean generalized travelling salesman problem (GTSP), in which the goal is to find a tour that visits one element from each of a collection of point sets. Our results characterize the long-term, asymptotic behavior of the tour length when a large number of points are sampled, and give rise to a number of counterintuitive managerial insights. 3 - Distributionally Robust Travelling Salesman Problem With Wasserstein Distance Mehdi Behroozi, Visiting Scholar, University of Southern California, Los Angeles, CA, United States, mehdi.behroozi@usc.edu, John Gunnar Carlsson Recent research on the robust and stochastic Euclidean travelling salesman problem has seen many different approaches for describing the region of uncertainty, such as taking convex combinations of observed demand vectors or imposing constraints on the moments of the spatial demand distribution. In this paper, we consider a distributionally robust version of the Euclidean travelling salesman problem in which we compute the worst-case spatial distribution of demand against all distributions whose Wasserstein distance to an observed demand distribution is bounded from above. This constraint allows us to circumvent common overestimation that arises when other procedures are used.

MD20 106C-MCC Recent Developments in Multistage Stochastic Programming

Invited: Tutorial Invited Session Chair: Alan King, IBM Research, 1, Yorktown Heights, NY, 15, United States, kingaj@us.ibm.com 1 - Recent Developments In Multistage Stochastic Programming Alan King, IBM Research, Yorktown Heights, NY, 10, United States, kingaj@us.ibm.com Multistage stochastic programming is a framework for applying large scale optimization technologies to multiperiod decision making under uncertainty. This talk will review the past decade and a half’s developments in multistage stochastic programming, including risk functionals, Stochastic Dual Dynamic Programming, time-consistent risk measures, and quantization of scenario trees.

MD21 107A-MCC Healthcare Information Systems Sponsored: Health Applications Sponsored Session

Chair: Yi-Chin Lin, Hofstra University, Hempstead, Weller Hall 032, Hempstead, NY, 11549-1000, United States, yichin.kl@gmail.com Co-Chair: Rema Padman, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, United States, rpadman@cmu.edu 1 - Physician Quality And Online Reviews Danish Saifee, PhD Student, Naveen Jindal School of Management/The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080, United States, dxs121230@utdallas.edu, Indranil Bardhan, Eric Zheng We investigate how online reviews and physician competence differ in terms of their impact on clinical outcomes. Using a panel of COPD patients, we find that physicians with higher competence exhibit better patient outcomes.

214