2015 Informs Annual Meeting

TD20

INFORMS Philadelphia – 2015

TD18 18-Franklin 8, Marriott Recent Advances in First Order Methods for Large- Scale Optimization Cluster: Modeling and Methodologies in Big Data Invited Session Chair: Mingyi Hong, Iowa State University, 3015 Black Engineering, Ames, IA, 50011, United States of America, mingyi@iastate.edu 1 - On the Information-adaptive Variants of the Admm: An Iteration Complexity Perspective Shuzhong Zhang, Professor, University of Minnesota, Department of Industrial and Systems Eng, Minneapolis, MN, 55455, United States of America, zhangs@umn.edu, Xiang Gao, Bo Jiang We present a suite of variants of the ADMM, where the trade-offs between the required information on the objective and the computational complexity are explicitly given. The new variants allow the method to be applicable on a much broader class of problems where only noisy estimations of the gradient or the function values are accessible, yet the flexibility is achieved without sacrificing the computational complexity bounds. 2 - An Optimal Randomized Incremental Gradient Method Guanghui Lan, University of Florida, Gainesville, FL, United States of America, glan@ise.ufl.edu We present a randomized incremental gradient method and show that this algorithm possesses unimprovable rate of convergence for convex optimization. We provide a natural game theoretic interpretation for this method as well as for the related Nesterov’s optimal method. We also point out the situations when this randomized algorithm can significantly outperform the deterministic optimal method. 3 - On the Expected Convergence of Randomly Permuted ADMM Ruoyu Sun, Stanford University, Menlo Park, CA, 94025, United States of America, sundirac@gmail.com, Zhi-Quan Luo, Yinyu Ye Recently, it has been shown that the direct extension of the alternating direction method of multipliers (ADMM) to the multi-block case fails to converge when solving a simple square system of linear equations. In this paper, however, we prove that, if in each step one randomly and independently permutes the updating order of any given number of blocks, the method will converge in expectation for solving the square system of linear equations. 4 - Alternating Direction Method of Multipliers for Distributed Sparse Principal Component Analysis Davood Hajinezhad, Iowa State University, 62 B Schilletter village, Ames, IA, 50010, United States of America, dhaji@iastate.edu, Mingyi Hong We propose distributed algorithms to perform sparse PCA. They are quite flexible, in the sense that they are able to handle different forms of data partition (i.e., partition across rows or columns of the data matrix). Numerical experiments based on both real and synthetic data sets, conducted on high performance computing clusters, demonstrate the effectiveness of our approaches. TD19 19-Franklin 9, Marriott Network Inference Sponsor: Computing Society Sponsored Session Chair: Nedialko Dimitrov, Assistant Professor, UT Austin, University of Texas at Austin, Austin, United States of America, ned@austin.utexas.edu 1 - Fast, Approximate Inference on Graphical Models by Reducing Treewidth Areesh Mittal, University of Texas at Austin, 1626 West 6th St. Apt. F, Austin, TX, 78703, United States of America, areesh0612@gmail.com, Nedialko Dimitrov Complexity of exact inference algorithms in graphical models is exponential in treewidth. We develop technique to perform approximate inference by removing edges and updating factors, leading to reduced treewidth. We prove bounds on error in approximation. Finding updated factors involves solving a geometric program (GP) with exponential number of constraints. We develop row generation technique to solve the GP. We demonstrate the results on discrete graphical models applied to social networks.

2 - Non-aggressive Adaptive Traffic Routing Madhushini Narayana Prasad, Graduate Research Assistant, Cockrell School of Engineering, University of Texas at Austin,

Austin, TX, 78712, United States of America, madhushini@utexas.edu, Nedialko Dimitrov

Routing a person through a traffic network presents a dilemma to choose between fixed route which is an easier to navigate route and adaptive route which minimizes the travel time by adjusting to the traffic conditions. We investigate methods for non-aggressive, adaptive routing that is middle-ground seeking the best of both these extremes, i.e. adaptive routes restricted in number of route shifts allowed at a critical juncture, and investigate the trade-offs between the extremes. 3 - Social Network Echo Chambers and Popularity Yinhan Liu, University of Texas Austin, 1901 Crossing Place #3301, Austin, TX, 78741, United States of America, yinhan.liu@utexas.edu, Nedialko Dimitrov Social network users often have the goal of building a large follower base. Some users are members of what we term echo chambers, a small group of users that re-share each other’s messages. We present an empirical study on the impact of echo chambers on the popularity of users using historical data from Twitter. Specific questions we address are: Does echo chamber membership increase re- shares outside the echo chamber? Does echo chamber membership increase follower base? Chair: Linna Du, Data Scientist, CACS, 2259 Adam Clyton Powell, New York, NY, 10027, United States of America, linna.du@gmail.com 1 - Success Drivers of Online Equity Crowdfunding Campaigns for Unaccredited Investors Anna Lukkarinen, Aalto University, P.O. Box 21220, Helsinki, 00076, Finland, anna.lukkarinen@aalto.fi, Jeffrey Teich, Hannele Wallenius, Jyrki Wallenius Using data from a leading equity crowdfunding platform in Northern Europe, we explore success factors of campaigns. The results suggest that the investment criteria traditionally used by professional investors are not of prime importance for success in equity crowdfunding. Instead, success is related to pre-selected crowdfunding campaign characteristics and networks. Understanding the success factors of online equity crowdfunding campaigns is important to the design of online platforms. 2 - The Mover-Stayer Process for the Credit Data Using the credit data set, coming from the European bank, we estimate the mover-stayer model, which is an extension of the Markov chain. This model assumes that the population is heterogeneous: there are “stayers” and “movers”. “Movers” evolve according to a Markov Chain with the one-step transition matrix, while “stayers” never leave their initial states. The probability of a customer being a stayer in a paid up state is modeled using the logistic regression. 3 - Monopolistic Dealer Versus Broker: Impact of Proprietary Trading with Transaction Fees Yuan Tian, Ryukoku University, 67 Tsukamoto-cho, Fukakusa, Fushimi-ku, Kyoto, Japan, tian@econ.ryukoku.ac.jp, Katsumasa Nishide We consider a one-period financial market with a monopolistic dealer/broker and an infinite number of investors. While the dealer (with proprietary trading) simultaneously sets both the transaction fee and the asset price, the broker (with no proprietary trading) sets only the transaction fee, given that the price is determined according to the market-clearing condition among investors. We effectively demonstrate how proprietary trading affects market equilibrium and welfare of investors. 4 - A Data Analytics Based Approach for Building 360 View of Banking Customer Tianzhi Zhao, IBM, Diamond Bld, ZGC Software Park, Beijing, China, zhaotzhi@cn.ibm.com, Zhen Huang, Ming Xie, Bing Shao, Yuhang Liu, Jian Xu, Wenjun Yin, Yuhui Fu Banks today are experiencing transformation from product centricity to customer centricity. With advent of big data, it enables banks to fully and deeply understand customers by building 360 degree customer view. In this paper, a data analytics based approach for 360 degree view of banking customer is proposed. It can help banks quick build customer centricity for customer segments, targeted marketing, personalized recommend, etc. Anna Matuszyk, Assistant Professor, Warsaw School of Economics, Niepodleglosci 162, Warsaw, 02-554, Poland, amatuszyk@matuszyk.com, Halina Frydman TD20 20-Franklin 10, Marriott Banking and Insurance Contributed Session

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