A. Veremyev, V. Boginski, P.A. Krokhmal, and D.E. Jeﬀcoat. Dense Percolation in LargeScale Mean-Field Random Networks Is Provably “Explosive”. PLoS ONE 7(12): e51883, 2012. DOI:10.1371/journal.pone.0051883.
O. Shirokikh, G. Pastukhov, V. Boginski, and S. Butenko. Computational study of the U.S. stock market evolution: A rank correlation-based network model. Computational Management Science, 2012 (accepted). DOI: 10.1007/s10287-012-0160-4.
J. Pattillo, A. Veremyev, S. Butenko, and V. Boginski. On the maximum quasi-clique problem. Discrete Applied Mathematics, 2012 (accepted). DOI: 10.1016/j.dam.2012.07.019.
A. Kammerdiner, A. Sprintson, E.L. Pasiliao, and V. Boginski. Optimization of discrete broadcast under uncertainty using conditional value-at-risk. Optimization Letters, 2012 (accepted). DOI: 10.1007/s11590-012-0542-0.
G. Pastukhov, A. Veremyev, V. Boginski, and E.L. Pasiliao. Optimal design and augmentation of strongly attack-tolerant two-hop clusters in directed networks. Journal of Combinatorial Optimization, 2012 (accepted). DOI: 10.1007/s10878-012-9523-6.
M. Carvalho, A. Sorokin, V. Boginski, and B. Balasundaram. Topology design for on-demand dualpath routing in wireless networks. Optimization Letters, 2012 (accepted). DOI: 10.1007/s11590-012-0453-0.
A. Veremyev and V. Boginski. Identifying Large Robust Network Clusters via New Compact Formulations of Maximum k-club Problems. European Journal of Operational Research, 218:316–326, 2012.
S. Stefan, M. Ehsan, W. Pearson, A. Aksenov, V. Boginski, B. Bendiak, and J. Eyler. Diﬀerentiation of Closely Related Isomers: Application of Data Mining Techniques in Conjunction with Variable Wavelength Infrared Multiple Photon Dissociation Mass Spectrometry for Identiﬁcation of Glucose-Containing Disaccharide Ions. Analytical Chemistry, 83(22):8468–8476, 2011.
A. Sorokin, V. Boginski, A. Nahapetyan, and P.M. Pardalos. Computational Risk Management Techniques for Fixed Charge Network Flow Problems with Uncertain Arc Failures. Journal of Combinatorial Optimization, 2011 (accepted). DOI: 10.1007/s10878-011-9422-2.
K. Kalinchenko, A. Veremyev, V. Boginski, D.E. Jeﬀcoat, and S. Uryasev. Robust connectivity issues in dynamic sensor networks for area surveillance under uncertainty. Paciﬁc Journal of Optimization, 7(2): 235–248, 2011.
N. Boyko, T. Turko, V. Boginski, D.E. Jeﬀcoat, S. Uryasev, G. Zrazhevsky, and P.M. Pardalos. Robust Multi-Sensor Scheduling for Multi-Site Surveillance. Journal of Combinatorial Optimization, 22(1): 35–51, 2011.
V. Boginski, C.W. Commander, and T. Turko. Polynomial-time Identiﬁcation of Robust Network Flows under Uncertain Arc Failures. Optimization Letters, 3(3):461–473, 2009.
Techniques for Sensor Networks, Algorithms, 2: 565–581, 2009. A. Arulselvan, G. Baourakis, V. Boginski, E. Korchina, and P.M. Pardalos. Analysis of Food Industry Market using Network Approaches. British Food Journal, 110(9): 916–928, 2008.
C. D. Dang and G. Lan, “On the Convergence Properties of Non-Euclidean Extragradient Methods for Variational Inequalities with Generalized Monotone Operators“, 2012, technical report, to be submitted.
A. Romich, G. Lan, and J.C. Smith, “Optimizing Placement of Stationary Monitors“, Octobor 2012, submitted for publication.
S. Ghadimi and G. Lan, “Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming“, June 2012, submitted for publication. (Source Code and Instances.) (This paper won the first place in the 2012 INFORMS Junior Faculty Interest Group (JFIG) paper competition.)
G. Lan, “Level methods uniformly optimal for composite and structured nonsmooth convex optimization“, April 2011, submitted to Mathematical Programming (under revision).
G. Lan, “Bundle-type methods uniformly optimal for smooth and nonsmooth convex optimization“, December 2010, submitted to Mathematical Programming (under revision).
G. Lan and R.D.C. Monteiro, ”Iteration-complexity of first-order augmented Lagrangian methods for convex programming“, July 2009, submitted to Mathematical Programming (under revision).
S. Ghadimi and G. Lan, “Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization, II: shrinking procedures and optimal algorithms“, July 2010, submitted to SIAM Journal on Optimization (Under second-round review).
C.D. Dang, K. Dai and G. Lan, A Linearly Convergent First-order Algorithm for Total Variation Minimization in Image Processing, International Journal of Bioinformatics Research and Applications (to appear), 2013. (A special issue for the International Conference on Computational Biomedicine in Gainesville, Florida, February 29 – March 2, 2012.).
G. Lan and R.D.C. Monteiro, “Iteration complexity of first-order penalty methods for convex programming“, Mathematical Programming, 138 (1), 2013, 115-139.
S. Ghadimi and G. Lan, “Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization, I: a generic algorithmic framework“, SIAM Journal on Optimization, 22 (2012) 1469-1492.
G. Lan, A. Nemirovski, and A. Shapiro, “Validation analysis of mirror descent stochastic approximation method“, Mathematical Programming, 134 (2), 2012, 425-458.
G. Lan, “An optimal method for stochastic composite optimization“, Mathematical Programming, 133 (1), 2012, 365-397. (A previous version of the paper entitled “Efficient methods for stochastic composite optimization” won 2008 INFORMS ICS student paper competition and George Nicholson Prize Competition second place.)
G. Lan, Z. Lu and R.D.C. Monteiro, “Primal-dual first-order methods with O(1/ε) iteration complexity for cone programming“, Mathematical Programming, 126 (2011), 1-29. Code and Instances.
G. Lan, R.D.C. Monteiro and T. Tsuchiya, ”A polynomial predictor-corrector trust-region algorithm for linear programming“, SIAM Journal on Optimization 19 (2009) 1918-1946.
A. Nemirovski, A.Juditsky, G. Lan, and A. Shapiro, “Robust stochastic approximation approach to stochastic programming“, SIAM Journal on Optimization 19 (2009), 1574-1609.