# 2015 Informs Annual Meeting

SD19

INFORMS Philadelphia – 2015

3 - Evacuation Modeling and Betweenness Centrality Chrysafis Vogiatzis, Assistant Professor, North Dakota State University, 1410 14th Avenue North, Room 202 Civil & Industrial Engineering, Fargo, ND, 58102, United States of America, chvogiat@ufl.edu Disaster management and evacuation modeling are vital for the welfare of modern urban societies. However, it is true that evacuating large urban areas is a very difficult problem due to the large scale of operations and the dynamic nature of most hazard phenomena. In this talk, we present two novel islanding techniques with the goal of decomposing large-scale network problems to smaller ones of manageable size. Computational results are also presented to show the success of our methodologies. 4 - Variable Versus Fixed Congestion Pricing under Day-to-Day Traffic Dynamics Zhengtian Xu, University of Florida, 511 Weil Hall, Gainesville, FL, 32611, United States of America, zhengtianxu@ufl.edu, Yafeng Yin This paper compares the effectiveness of fixed and variable congestion pricing on evolving network flows to a target flow distribution under day-to-day traffic dynamics. Fixed pricing charges constant tolls while variable pricing updates tolls every day based on recent days’ traffic conditions. The paper proves that the latter does not necessarily perform better than the former and provides conditions when this situation arises. Numerical experiments are presented to demonstrate the comparison. 5 - Vulnerability Modeling and Analysis of Complementary Transportation Systems Liu Hong, Associate Professor, Huazhong University of Science and Technology, Room W.308, South 1,1037#, Luoyu Road,, Wuhan, China, liu.hong.science@gmail.com, Min Ouyang, Xiaozheng He This paper proposes a network-based approach to model and analyze the vulnerability of complementary transportation systems, with main focus on quantifying their complementary strength, assessing dynamic vulnerability and discussing critical components. Two level complementary systems are used to illustrate the tractability and effectiveness of the proposed method, including railway and airline system in China (national level), metro and bus system in Wuhan city in China (Urban level). SD18 18-Franklin 8, Marriott Theory and Applications of Coordinate Descent and Alternating Direction Methods Cluster: Modeling and Methodologies in Big Data Invited Session Chair: Brendan Ames, Assistant Professor, University of Alabama, Department of Mathematics, Box 870350, Tuscaloosa, AL, 35487, United States of America, bpames@ua.edu 1 - Block Coordinate Stochastic Gradient Method Yangyang Xu, University of Minnesota, Institute of Math and Application, Minneapolis, MN, United States of America, xuyang.gucas@gmail.com, Wotao Yin Stochastic gradient (SG) can quickly solve a problem with many components in the objective to a moderate accuracy, and block coordinate descent (BCD) method can quickly solve problems with multiple (blocks of) variables. In this talk, we will introduce a block stochastic gradient (BSG) method that combines SG and BCD for problems with many components in the objective and with multiple blocks of variables. We will show its convergence and demonstrate its superiority over SG and BCD. 2 - Proximal Methods for Sparse Discriminant Analysis Brendan Ames, Assistant Professor, University of Alabama, Department of Mathematics, Box 870350, Tuscaloosa, AL, 35487, United States of America, bpames@ua.edu We consider the problem of simultaneously performing classification and feature selection in the high-dimension, low sample size setting, where the number of features of our data is large while the number of samples is limited. In particular, we propose an alternating direction method for performing optimal scoring-based linear discriminant analysis with a sparseness criterion, where proximal gradient methods are used to update the decision variables in the alternating direction framework.

3 - Iteration Complexity Analysis of Block Coordinate Descent Methods

Mingyi Hong, Iowa State University, 3015 Black Engineering, Ames, IA, 50011, United States of America, mingyi@iastate.edu We provide a unified iteration complexity analysis for a family of block coordinate descent methods. We unify these algorithms under the so-called Block Successive Upper-bound Minimization framework, and show that they achieve a global sublinear iteration complexity. Moreover, for the case of block coordinate minimization where each block is minimized exactly, we establish the sublinear convergence rate of O(1/r) without per block strong convexity assumption. 4 - Self Equivalence of the Alternating Direction Method of Multipliers Ming Yan, Assistant Professor, Michigan State University, 220 Trowbridge Rd, East Lansing, MI, 48824, United States of America, yanm@math.ucla.edu In this talk, we show that ADM applied to a primal formulation is equivalent to ADM applied to its Lagrange dual; ADM is equivalent to a primal-dual algorithm applied to the saddle-point formulation of the same problem. In addition, when one of the two objective functions is quadratic, we show that swapping the update order of the two primal variables in ADM gives the same algorithm. SD19 19-Franklin 9, Marriott Computational Data Analytics Sponsor: Computing Society Sponsored Session Chair: Dorit Hochbaum, University of California, IEOR, “Etcheverry Hall, Berkeley, Ca, 94720, United States of America, dhochbaum@berkeley.edu 1 - Improved Algorithms Solving Generalizations of Isotonic Median Regression Cheng Lyu, University of California, 4141 Etcheverry Hall, Berkeley, CA, 94720, United States of America, chenglu@berkeley.edu, Dorit Hochbaum Isotonic median regression (IMR) problem is a well-known statistical nonparametric regression problem. We address here generalizations of IMR for which we devise efficient algorithms. Our algorithms improve on the best previously known complexity for the special case of IMR. Some applications of independent interest of the generalized problems include total variation on a Boris Goldengorin, C. Paul Stocker Visiting Professor, Ohio University, 283 Stocker Center, Athens, OH, United States of America, goldengorin@gmail.com In this talk we show how to aggregate and truncate the numerical data in huge m (rows) times n (columns) tables by means of ordering the entries within their columns in a non-decreasing (non-increasing) order. The ordered columns can be represented by the corresponding permutations and differences between the neighboring entries. Our computational study shows that for complete networks containing thousands of vertices we are able to reduce the number of entries by more than 90%. 3 - Adjacency-clustering for Yield Prediction in Integrated Circuit Manufacturing (ICM) Dorit Hochbaum, University of California, IEOR, Etcheverry Hall, Berkeley, CA, 94720, United States of America, dhochbaum@berkeley.edu, Sheng Liu Accurate yield prediction in ICM enables early detection of processing problems. It is noted that defects tend to be clustered and a circuit likely to be defective if its neighbors are defective. This Neighborhood Effect NE is not captured in traditional spatial modeling approaches. We model the yield prediction problem with NE as a Markov Random Field model (MRF). A comparison with leading approaches (GLM and GLMM) demonstrates that MRF provides superior accuracy and time complexity. path, signal processing, and nearly-isotonic regression. 2 - Big Data Aggregation and Truncation by Pseudo-boolean Polynomials

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