2016 INFORMS Annual Meeting Program

TD07

INFORMS Nashville – 2016

TD05 101E-MCC Forest Management Sponsored: Energy, Natural Res & the Environment II Forestry Sponsored Session Chair: Aaron Bradley Hoskins, United States Naval Research Laboratory, 7220 Briarcliff Drive, Springfield, VA, 22153, United States, abh318@msstate.edu Co-Chair: Sándor F. Tóth, University of Washington, School of Environmental & Forest Sciences, Seattle, WA, 9, United States, oths@uw.edu 1 - Stochastic Forestry Planning Problem Using Progressive Hedging Cristobal Pais, PhD Student, Instituto de Sistemas Complejos de Ingeniería, Domeyko 2367, Santiago, 8370397, Chile, cpais@ing.uchile.cl, Andres P Weintraub The forest planning problem with road construction consists in managing the production of a land divided into harvest cells. The goal is the maximization of the expected NPV for a tactical plan subject to uncertainties. A method is applied for generating scenario trees (price, demand and growth) with their probabilities. Non-anticipativity constraints are needed in the model to link scenarios. This problem is difficult to solve due to the number of scenarios. We have implemented Progressive Hedging which separates the problem by scenarios, with multiple adjustments to improve its solvability exploiting its parallel implementation. With this approach, instances up to 1000 scenarios were solved. 2 - Using Critical Component Detection In Graphs For Wildfire Fuel Management Dmytro Matsypura, The University of Sydney, Business School, Abercrombie Building H70, Sydney, 2006, Australia, dmytro.matsypura@sydney.edu.au We study the problem of wildfire fuel management. The problem is formulated as a non-linear optimization problem. We apply the formulations and objectives used for critical element detection in graphs. We present the results of simulations and empirical study. 3 - A Stochastic Programming Approach To Satellite Constellation Design Aaron B Hoskins, PhD Candidate, Mississippi State University, P.O. Box 9542, Starkville, MS, 39762, United States, abh318@msstate.edu, Hugh Medal A constellation of satellites is launched to collect data on wildfires. The time and location of the wildfires is not known at the time the satellites are launched into orbit. This research implements a stochastic programming algorithm to select the initial constellation design that minimizes the expected cost of maneuvering the satellites to collect data once the location of the wildfire has been realized. 4 - Optimal Carbon Sampling In Remote Forest Regions Sándor F. Tóth, University of Washington, School of Environmental & Forest Sciences, Seattle, WA, 9, United States, oths@uw.edu, Hans Eric Andersen We present a spatial optimization approach to find the best integrated ground and air sampling strategy for carbon in remote boreal forests. We minimize the expected variance on estimates of the mean carbon tonnage in six forest pools by optimal flight path selection for remote sensing and by optimal vehicle routing for ground calibration. We apply the model, which incorporates budgetary and logistical constraints, to the Tanana District of the U.S. Forest Service in the Interior of Alaska. TD06 102A-MCC Optimization for Large-Scale Learning Sponsored: Data Mining Sponsored Session Chair: Dzung Phan, IBM Research, Yorktown Heights, NY, United States, phandu@us.ibm.com 1 - Gradient Sliding For Structured Convex Optimization Yuyuan Ouyang, Clemson University, yuyuano@clemson.edu, Guanghui Lan We present a gradient sliding method for a class of structured convex optimization. In particular, we assume that the optimization problem has the structure of the sum of two components. The proposed method is capable of skipping the evaluation of one of the components in the problem, will preserving the overall iteration complexity. The proposed method can be applied to smooth optimization, bilinear saddle point optimization, and variational inequalities.

2 - Storm: Stochastic Optimization Using Random Models Matthew Joseph Menickelly, Lehigh University, mjm412@lehigh.edu In this talk, we will discuss work in developing an algorithmic framework (STORM) for the unconstrained minimization of a stochastic function, f. The framework is based on the class of derivative-free trust-region methods. It essentially requires that both the quality of random models of f and the error in a pair of point estimates - one at the current iterate and a second at the trial step - scale with the square of the trust region radius. We compare this approach to usual methods of stochastic optimization, e.g. stochastic gradient descent, and discuss possible applications of STORM in hyperparameter tuning and AUC optimization. 3 - Extended Gauss-Newton-Type Algorithms For Low-rank Matrix Optimization Quoc Tran-Dinh, University of North Carolina at Chapel Hill, quoctd@email.unc.edu We develop a Gauss-Newton framework for solving nonconvex optimization problems involving low-rank matrix variables. The algorithm inherits advanced features from classical Gauss-Newton method to this extension such as local linear and quadratic convergence. Under mild assumptions, we prove the local linear and quadratic convergence of our method.By incorporating with a linesearch, the algorithm has a global convergence guarantee to a critical point of problems. As a special case, we customize our framework to handle the symmetric case with provable convergence guarantees. We test our algorithms on various practical problems including matrix completion, and quantum tomography. 4 - Projection Algorithms For Nonconvex Minimization With Application To Sparse Principal Component Analysis Dzung Phan, IBM Research, phandu@us.ibm.com, William Hager, Jiajie Zhu We minimize a concave function over nonconvex sets, and propose a gradient projection algorithm (GPA) and an approximate Newton algorithm (ANA). Convergence results are established. In numerical experiments arising in sparse principal component analysis, it is seen that the performance of GPA is very similar to the fastest current methods. In some cases, ANA is substantially faster than the other algorithms, and gives a better solution. TD07 102B-MCC Emerging Topics on Internet of Things (IoT) and Data Analytics Sponsored: Data Mining Sponsored Session Chair: Chen Kan, Pennsylvania State University, University Park, PA, United States, cjk5654@psu.edu 1 - A Pomdp Approach For Optimal Alerting To Remotely Monitored Asthma Patients Considering Alert Compliance Issue Junbo Son, Assistant Professor, University of Delaware, Newark, DE, United States, sonjunbo@gmail.com, Shiyu Zhou Driven by the IoT, a smart asthma management (SAM) system has been implemented where rescue inhalers with a wireless connection record the inhaler usage and transmit the data to a centralized server. Based on the diagnosis result, the system may alert the patient for timely interventions. A crucial challenge is to decide when to alert the patients considering the fact that the patient may not comply the alert. This alert compliance issue complicates the decision process. In this research, a novel partially observable Markov decision process considering alert compliance is proposed and useful insights were found which would benefit both the asthma patients and company who run the SAM system. 2 - Integration Of Data-level Fusion Model And Kernel Methods For Degradation Modeling And Prognostic Analysis Changyue Song, University of Wisconsin-Madison, Madison, WI, United States, csong39@wisc.edu, Kaibo Liu Internet of things has enabled a data-rich environment with multiple sensors to monitor the degradation process of a unit in real time. As each sensor signal often contains partial and dependent information, data-level fusion methods have been developed that aim to construct a health index via the combination of multiple sensor signals. The existing data-level fusion methods are limited by only considering a linear fusion function, which may be insufficient to accurately characterize the complex relations of sensor signals in practice. This study fills the literature gap by integrating the kernel methods with data-level fusion approaches to incorporate nonlinear fusion functions.

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