2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
182
Acceleration Of A Communication Efficient Distributed Dual
Block Descent Algorithm
Chenxin Ma, Lehigh University, 200 West Packer Avenue,
Bethlehem, PA, 08801, United States,
Distributed optimization algorithms for very large-scale machine learning suffer
from communication bottlenecks. Confronting this issue, a communication-
efficient primal-dual coordinate ascent framework (CoCoA) and its improved
variant CoCoA+ have been proposed, achieving a convergence rate of O(1/t) for
solving empirical risk minimization problems with Lipschitz losses. In this paper,
we propose an accelerated variant of CoCoA+ and show that it has a rate of
O(1/t2) in terms of reducing dual suboptimality. Our analysis is also notable in
that our convergence rate bounds involve constants that, except in extreme cases,
are significantly reduced.
Primal-dual Interior-point Methods With Domain-driven Barriers
Mehdi Karimi, PhD Student, University of Waterloo, 200
University Avenue West, Department of Combinatorics and
Optimization, University of Waterloo, Waterloo, ON, N2L 3G1,
Canada, , Levent Tuncel
While primal-dual algorithms have yielded efficient solvers for convex
optimization problems in conic form over symmetric cones, many other highly
demanded convex optimization problems lack comparable solvers. To close this
gap, we develop infeasible-start primal-dual interior-point algorithms for convex
optimization problems in “domain-driven” formulation, which we show covers
many interesting optimization problems including the conic ones. After
presenting our techniques, we introduce our Matlab-based code that solves a
large class of problems including LP, SOCP, SDP, QCQP, Geometric programming,
and Entropy programming among others, and mention some numerical
challenges.
Upward Reward Perturbation For Reinforcement Learning
Zhengyuan Zhou, Stanford University, 160 Comstock Circle, Unit
106002, Stanford, CA, 94305, United States, ,
Ling Zhu, Benjamin Van Roy, Nicholas Bambos
Late Cancellation
Applying Case Queries To Network Clusters: Identifying The
Intussusception Signal In Rotashield Adverse Event Reports
Matthew Foster, ORISE Fellow, FDA, 10903 New Hampshire Ave,
Silver Spring, MD, 20993, United States,
We constructed a network of Medical Dictionary for Regulatory Activities
(MedDRA) Preferred Terms (PTs) from RotaShield adverse event reports and
calculated the eigenvector, betweenness, and closeness centrality metrics. We
used these metrics to cluster PTs and assessed the sensitivity of clusters using an
intussusception (IS) case definition and a variety of Standardized MedDRA
Queries. Clustering using eigenvector vs closeness centrality was superior to other
combinations, although all identified the IS signal before the July 1999 CDC
recommendation for discontinuation. The early detection of this safety signal
supports the potential use of our methodology for safety surveillance.
Neighborhood-based Reductions And Cuts For Signed Graphs
Christopher Muir, University of Tennessee - Knoxville,
2044 Wilkerson Road, Knoxville, TN, 37922, United States,
This research is concerned with improving solve times for instances of the
Balanced Subgraph problem. We discuss various data reduction techniques based
on the neighborhoods of vertices and on cut vertices. Additionally, we show that
certain structures often present in signed graphs can be exploited to allow for
faster solve times. Computational test results are also presented for previously
explored problems, including the toll-like problem from the MIPLIB 2010
instance library.
Monday, 1:30PM - 3:00PM
MC01
101A-MCC
Image and Shape Data Analysis
Sponsored: Data Mining
Sponsored Session
Chair: Chiwoo Park, Florida State University, 2525 Pottsdamer Street,
Tallahassee, FL, 32310-6046, United States,
Co-Chair: Kamran Paynabar, Assistant Professor, Georgia Institute of
Technology, 755 Ferst Drive, NW, Atlanta, GA, 30332, United States,
1 - State Space Model For Time-varying Density Estimation
Yanjun Qian, Texas A&M University, College Station, TX, 77840,
United States, Yu Ding, Jianhua Huang
In both scientific and industrial fields, estimating the time-varying density can
provide an important tool for the monitoring or research purpose. In our work,
we propose a new method for the time-varying density estimation based on the
state space model. Our method can learn the system parameters off-line, and
provides an on-line density curve updating from the observed histogram data. By
adding a spatial penalty term, we can guarantee the smoothness of the estimated
curves and prevent the over-fitting. At last, we show the application of our
method on the study of the nanocrystal growth process.
2 - Dynamic Network Modeling Of In-situ Image Profiles For
Statistical Process Control – Applications In Ultraprecision
Machining And Biomanufacturing Process
Chen Kan, Pennsylvania State University, PSU, University Park,
PA, 16802, United States, Hui Yang
Modern industries are investing in advanced imaging technology to increase
information visibility, cope with system complexity, and improve the quality and
integrity of system operations. Realizing the full potential of advanced imaging
technology for process monitoring and control hinges on the development of new
SPC methodologies. This paper presents a novel dynamic network methodology
for monitoring and control of high-dimensional imaging streams.
3 - Structured Point Cloud Data Modeling Via Regularized Tensor
Decomposition And Regression
Hao Yan, Georgia Institute of Technology, ,
Massimo Pacella, Kamran Paynabar
Due to the easy accessibility of the 3D metrology tools such as Coordinate
Measuring Machine or scanning tools, structured point cloud data is becoming
more and more popular. Therefore, modeling the structured point cloud is an
important task in many application domains. We model the structure point cloud
as tensor and propose regularized tucker decomposition and regularized tensor
regression to detect the variation patterns in the data and link these patterns to
the process variables. Furthermore, the performance of the proposed method is
evaluated through simulation and a real case study in the point cloud data in the
turning process.
4 - Statistical Analysis Of Preferential Orientations Of Two Shapes In
Their Aggregate
Ali Esmaieeli, Florida State University, Tallahassee, FL, 32304,
United States, Chiwoo Park, David Welch,
Roland Faller, Taylor Woehl, James Evans, Nigel Browning
Nanoscientists believe that adjacent nanoparticles aggregate with each other in
specific preferential directions. This phenomenon is known as oriented
attachment and can be studied by direct observations using dynamic electron
microscopy. These studies relied on manual and qualitative analysis up to now;
therefore, in this research we are proposing a statistical approach to study the
oriented attachment believing that certain geometries have specific preferential
orientation when they aggregate. We use multiple aggregation examples collected
from dynamic microscope data in order to examine the performance of our
approach.
MC01