2016 INFORMS Annual Meeting Program

TC17

INFORMS Nashville – 2016

TC15 104E-MCC Joint Session AI/SMA: Social Media Analytics and Big Network Data Sponsored: Artificial Intelligence Sponsored Session Chair: Bin Zhang, Assistant Professor, University of Arizona, 1130 E. Helen St, Tucson, AZ, 85715, United States, binzhang@arizona.edu 1 - Analyzing Heterogeneity Of User Participation Behavior In Online News Article Propagation Devi Bhattacharya, University of Arizona, dbhattac@email.arizona.edu The session will illustrate the diversity in users’ article sharing behavior on social media. The literature on social media based propagation although considerable generally undercompensates heterogeneity in users’ behavior incidental to the source or type of content being propagated. In contrast, our research provides extensive empirical evidence of multiplicity in users’ article sharing behavior by analyzing 5 different facets of user participation. Our research uses network theoretic and statistical techniques on a Twitter dataset of 35 news providers collected over 9 months to provide critical insights essential for modeling users’ news article sharing behavior on social media. 2 - Knowledge Discovery Using Disease Comorbidity Networks Karthik Srinivasan, University of Arizona, Tucson, AZ, 85719, United States, karthiks@email.arizona.edu, Faiz Currim, Sudha Ram Comorbidity or disease co-occurrence is the simultaneous presence of two or more diseases. A disease comorbidity network (DCN) can be constructed based on repeated evidence of two or more diseases co-occurring in anonymized patient visit datasets. We report on an empirical study using a large dataset of electronic health records to explore the characteristics of this network including community formation and structural measures. Our network analysis is used to reinforce existing medical knowledge about comorbidity and to reveal previously unknown comorbidity relationships. 3 - Social Networks In Online Games: The Impact Of Peer Influences On Repeat Purchase Bin Zhang, University of Arizona, binzhang@arizona.edu, Ruibin Geng, Xi Chen Consumers’ purchase decisions can be affected by both direct and indirect peer influences. However, there is no existing work about how these two types of influence impact repeat-purchase of. In this study, we fill such gap by investigating the interdependent repeat-purchase of online game players embedded in a social network. We build a new hierarchical Bayesian model that can support response variable following Poisson distribution and simultaneously include both types of peer influence, direct and indirect. Our empirical study yields interesting findings that peer influences have a significant impact on repeat purchase, but the directions of direct and indirect influences are different. TC16 105A-MCC Inverse Optimization: Applications Sponsored: Optimization, Optimization Under Uncertainty Sponsored Session Chair: Taewoo Lee, Rice University, 1, Houston, TX, United States, taewoo.lee@rice.edu 1 - Inverse Optimization For The Analysis Of Competitive Markets: An Application To The North American Fuel Market Michael Pavlin, Wilfrid Laurier University, mpavlin@wlu.ca In this talk we consider conditions under which inverse optimization can be used to analyze unobservable supply constraints underlying pricing of competitive markets. We present a study of price differentiation in the North American fuel market. 2 - Inverse Optimization For Intensity Modulated Proton Therapy Neal Kaw, University of Toronto, Toronto, ON, Canada, neal.kaw@mail.utoronto.ca, Timothy Chan, Albin Fredriksson Robust multiobjective optimization models can be used to determine treatment plans for intensity-modulated proton therapy (IMPT), which is a method of cancer therapy. The uncertainty set in such a model is a set of scenarios. In this work, we propose a novel inverse optimization model to determine objective weights and a set of scenarios, such that a historical treatment plan is optimal with respect to a given robust IMPT optimization model. We apply our model to prostate cancer data.

3 - Data-driven Objective Selection In Multi-objective Optimization: Inverse Optimization Approach Taewoo Lee, Rice University, 6100 Main MS-134, Houston, TX, 77005, United States, taewoo.lee@rice.edu, Tayo Ajayi, Andrew J Schaefer Performance of a multi-objective optimization problem largely depends on the choice of objectives. Currently, objectives are typically chosen in a trial-and-error manner based on subjective beliefs. We propose an inverse optimization approach to learn from data and determine a minimal set of objectives for a multi-objective optimization problem such that the problem generates solutions that are comparable to the given data. We provide theoretical properties of the methodology in comparison with regression, and establish a relationship with the traditional best subset regression approach. We demonstrate the methodology in cancer therapy applications. TC17 105B-MCC Stochastic Optimization for Large-Scale Learning General Session Chair: Qihang Lin, the University of Iowa, 21 East Market Street, PBB, S380, Iowa City, IA, 52245, United States, qihang-lin@uiowa.edu 1 - Adadelay: Delay Adaptive Distributed Stochastic Optimization Adans Wei Yu, Carnegie Mellon University, Pittsburgh, PA, United States, weiyu@cs.cmu.edu We develop distributed stochastic optimization algorithms under a delayed gradient model in which server nodes update parameters and worker nodes compute stochastic (sub)gradients. Our setup is motivated by the behavior of real- world distributed computation systems; in particular, we analyze a setting wherein worker nodes can be differently slow at different times. In contrast to existing approaches, we do not impose a worst-case bound on the delays experienced but rather allow the updates to be sensitive to the actual delays experienced. This sensitivity allows use of larger stepsizes, which can help speed up initial convergence without having to wait too long for slower machines. 2 - Rsg: Beating Subgradient Method Without Smoothness And Strong Convexity Tianbao Yang, the University of Iowa, tianbao-yang@uiowa.edu, Qihang Lin In this paper, we propose novel deterministic and stochastic {\bf R}estarted {\bf S}ub{\bf G}radient (RSG) methods that can find an $\epsilon$-optimal solution for a broad class of non-smooth and non-strongly convex optimization problems faster than the vanilla deterministic or stochastic subgradient method (SG). We show that for non-smooth and non-strongly convex optimization, RSG can reduce the dependence of SG’s iteration complexity on the distance to the optimal set of the initial solution to that of points on the $\epsilon$-level set. For a family of non-smooth and non-strongly convex optimization problems whose epigraph Ruoyu Sun, Stanford University, ruoyus@stanford.edu, Yinyu Ye We establish several complexity bounds of cyclic coordinate descent (C-CD) for quadratic minimization, and prove that these bounds are almost tight. Although some existing examples showed C-CD can be much slower than randomized coordinate descent (R-CD), in many practical examples C-CD is faster than R-CD, making it unclear whether the worst-case complexity of C-CD is better or worse than R-CD. One of our bounds shows that C-CD can be O(n^2) times slower than R-CD in the worst case. One difficulty with the analysis of the constructed example is that the spectral radius of a non-symmetric iteration matrix does not necessarily constitute a lower bound for the convergence rate. 4 - Homotopy Smoothing For Structured Non-smooth Problems Qihang Lin, Assistant Professor, University of Iowa, Iowa City, IA, 52242, United States, qihang-lin@uiowa.edu, Yi Xu, Yan Yan, Tianbao Yang We develop a novel homotopy smoothing (HOPS) algorithm for non-smooth convex optimization where the non-smooth term has a max-structure. The best known iteration complexity of first-order method for this problem is O(1/epsilon). We show that the proposed HOPS achieves a lower iteration complexity of O (1/epsilon^(1-theta)) with theta in (0, 1] capturing the local sharpness of the objective function around the optimal solutions. The HOPS algorithm uses Nesterov’s smoothing method in stages and gradually decreases the smoothing parameter until it yields a sufficiently good approximation of the original function. is a polyhedron, we further show that RSG could converge linearly. 3 - Worst-case Complexity Of Cyclic Coordinate Descent

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