2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - Choosing A Solution Strategy For Discrete Quadratic Optimization
Robert Fourer, AMPL Optimization Inc.,
The combination of integer variables with quadratic objectives and constraints is a
powerful formulation tool. But when it comes to solving the resulting
optimization problems, there are numerous good approaches but no one best way
— even in simpler cases where the objective is convex or the constraints are
linear. Both linearization of quadratic terms and quadratic generalization of linear
methods turn out to be preferable in some circumstances. This presentation
exhibits a variety of examples to illustrate the questions that should be asked and
the decisions that must be made in choosing an effective formulation and solver.
3 - Performance Tuning For Cplex’s Spatial Branch-and-bound Solver
For Global Nonconvex Mixed Integer Quadratic Programs
Ed Klotz, IBM,
MILP solvers have been improving for more than 40 years, and performance
tuning tactics regarding both adjusting solver strategies and model formulations
have evolved as well. State-of-the-art global nonconvex MIQP solvers have
improved dramatically in recent years, but they lack the benefit of 40 years of
evolution. Also, they use a broader notion of branching that can create different
performance challenges. This talk will assess the effectiveness of existing MILP
tuning tactics for solving nonconvex MIQPs, as well as consider more specific
strategies for this challenging type of problem.
4 - On The Benefits Of Enumeration Within An
Optimization Framework
Alexandra M Newman, Colorado School of Mines, Golden, CO,
While the branch-and-bound algorithm, and associated enhancements such as
cuts and heuristics, vastly dominates the performance of pure enumeration for
obtaining optimal solutions for (mixed) integer programming problems, this basic
strategy can sometimes expedite solutions. Specifically, for cases in which
enumeration of partial solutions leaves an exploitable structure in place, a small
amount of enumeration over fast-solving subproblems drastically reduces solution
time. We demonstrate using examples from mining (with reformulations of a
monolith) and interdiction (with Benders decomposition).
TD14
104D-MCC
Predictive Analytics: Big Data with Purpose
Sponsored: Analytics
Sponsored Session
Chair: Rob Lantz, Novetta Solutions, 7921 Jones Branch Drive,
McLean, VA, 22102, United States,
1 - Volatility-based Metrics To Analyze Network Traffic Over Time:
Situational Awareness And Anomaly Detection
Soumyo Moitra, Carnegie Mellon University, Software
Engineering Institute, Pittsburgh, PA, 15213, United States,
We develop some metrics to analyze temporal data that investigate different
aspects of volatility. The metrics would be useful for monitoring network traffic
data as well as other time series data. We discuss the motivation for the metrics
and apply them to simulated data to demonstrate the properties of the metrics
and to show how they can be used to derive insights into traffic patterns. Results
under different scenarios are presented and compared.
2 - Security And Multidimensional Prediction Problems
Anthony Boyles, Novetta, 7921 Jones Branch Drive, McLean, VA,
22102, United States,
Modern security forecasting often requires comparatively high-dimensional
predictions: for example, a drug bust can only be made at a specific location
during the window of time that the drugs will be in that location. A forecaster
must therefore be able to predict in at least three dimensions: latitude, longitude,
and time. We examine techniques to reduce the complexity and increase
predictive power of models grappling with this problem.
3 - Smart Strategies For Matching Big Data
Matthew Teschke, Novetta, 1111 Arlington Blvd., Apt. 707,
Arlington, VA, 22209, United States,
Matching, such as entity resolution, is often a required part of an analysis;
however, in a Big Data environment this can be a challenging task for analysts.
Naive matching implementations are not computationally feasible when record
counts are measured in millions or even billions, so a different approach is
needed. Blocking is a strategy for making these problems tractable, taking them
from an n-squared time to near-linear time. This presentation will provide an
overview of blocking, its applications, and details of implementation.
4 - Predicting Return Abuse With Data Analytics
Michael Ketzenberg, Associate Professor, Texas A&M University,
Mail Stop 4217, College Station, TX, 77845, United States,
We apply data analytics to the transactional history of a large national retailer to
identify the characteristics of customers who abuse return policies and then utilize
this information to develop and test predictive models to help prevent such abuse.
TD15
104E-MCC
Joint Session AI/Analytics: Machine Learning for
Public Policy
Sponsored: Artificial Intelligence/Analytics
Sponsored Session
Chair: John J Nay, Vanderbilt University, PMB 351826
2301 Vanderbilt Place, Nashville, TN, 7235-1826, United States,
1 - Text Modeling For Understanding And Predicting The
Federal Government
John J Nay, Vanderbilt University,
We describe the application of neural network based language modeling to better
understand and predict the federal government. We embed institutions and the
words from their policy documents into shared “semantic” space to explore
differences across institutions with respect to policy topics. We apply our method
to learn useful representations of the Supreme Court, House, Senate, and
President. We also develop a machine learning approach to forecasting the
probability that any bill will be enacted. The model outperforms competitor
models across the three validation measures and is systematically analyzed to
investigate textual and contextual factors predicting enactment.
2 - Simple Rules For Pretrial Release Decisions
Jongbin Jung, Stanford University,
While predictive machine learning techniques might seem appealing for policy
decisions, their opaque nature often render them inappropriate for the task. We
investigate the possibility of constructing transparent and interpretable rules, and
evaluate their performance compared to complex models in the context of pretrial
release decisions. Although complex models generally outperform simple rules,
we find the difference to be arguably small, especially considering the benefit of
transparent rules being more likely to be implemented.
3 - Data-driven Agent-based Modeling
Yevgeniy Vorobeychik, Vanderbilt University,
Haifeng Zhang
Agent-based modeling has been extensively used to study innovation diffusion.
We develop a novel methodology for data-driven agent-based modeling that
enables both calibration and rigorous validation at multiple scales. Our first step is
to learn a model of individual agent behavior from individual event data. We then
construct an agent-based simulation with the learned model embedded in
artificial agents, and proceed to validate it using a holdout sequence of collective
adoption decisions. We instantiate our model in the context of solar PV adoption,
and utilize it to evaluate and optimize policy decisions aimed at promotion of
rooftop solar.
TD16
105A-MCC
Joint Session DM/Optimization Under Uncertainty:
Optimization in Data Mining and Machine Learning
Sponsored: Optimization, Optimization Under Uncertainty/DM
Sponsored Session
Chair: Ozgu Turgut, Wayne State University, 1230 Wisteria Drive,
A321, Ann Arbor, MI, 48104, United States,
Co-Chair: Michael Hahsler, Southern Methodist University, Dallas, TX,
United States,
1 - Sequential Aggregation-disaggregation Optimization Methods For
Data Stream Mining
Michael Hahsler, SMU, Dallas, TX, 75275, United States,
Young Woong Park
Clustering-based iterative algorithms to solve certain large optimization problems
have been proposed in the past. The algorithms start by aggregating the original
data, solving the problem on aggregated data, and then in subsequent steps
gradually disaggregate the aggregated data. In this contribution, we investigate
the application of aggregation-disaggregation on data streams, where the
disaggregation steps cannot be explicitly performed on past data, but has to be
performed sequentially on new data.
TD14