This page contains the publications for the OptSys proposal.

[1] B. O. Palsson. Systems Biology: Constraint-based Reconstruction and Analysis. Cambridge University Press, NY, 2015.

[2] T. Dandekar, A. Fieselmann, S. Majeed, Z. Ahmed. Software applications toward quantitative metabolic flux analysis and modeling. Briefings in Bioinformatics, pages 1–17, 2012.

[3] Z.D. Stephens, S.Y. Lee, F. Faghri, R.H. Campbell, C. Zhai, M.J. Efron, R. Iyer, M.C. Schatz, S. Sinha, G.E. Robinson. Big data: astronomical or genomical? PLOS Biology, 3(7), 2015.

[4] J. Sung, V. Haleb, A.C. Merkelb, P.J. Kima, N. Chia. Metabolic modeling with big data and the gut microbiome. Applied & Translational Genomics, 2016.

[5] I. Thiele, B.O. Palsson. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature Protocols, 5:93–121, 2010.

[6] J. Schellenberger, R. Que, R. M. T. Fleming, I. Thiele, J. D. Orth, A. M. Feist, D. C. Zielinski, A. Bordbar, N. E. Lewis, S. Rahmanian, et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature Protocols, 6(9):1290–1307, 2011.

[7] I. Thiele, R.M. Fleming, A. Bordbar, R. Que, B.O. Palsson. A systems biology approach to the evolution of codon use pattern. Submitted, 2011.

[8] I. Thiele, N. Jamshidi, R.M.T. Fleming, B.O. Palsson. Genome-scale reconstruction of Escherichia coli’s transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLoS Computational Biology, 5(3):e10003129, 2009.

[9] I. Thiele, R.M. Fleming, R. Que, A. Bordbar, D. Diep, B.O. Palsson. Multiscale modeling of metabolism and macromolecular synthesis in E. coli and its application to the evolution of codon usage. PLoS ONE, 7(9):e45635, 2012.

[10] J.D. Orth, I. Thiele, B.O. Palsson. What is flux balance analysis? Nature Biotechnology, 28(3):245–248, 2010.

[11] I. Thiele, R.M.T. Fleming, A. Bordbar, J. Schellenberger, B.O. Palsson. Functional characterization of alternate optimal solutions of Escherichia coli’s transcriptional and translational machinery. Biophysical Journal, 98(10):2072–2081, 2010.

[12] R.M.T. Fleming, C.M. Maes, M.A. Saunders, B.O. Palsson, "A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks", Journal of Theoretical Biology 292 (2012), pp. 71--77.

[13] S. Gudmundsson, I. Thiele. Computationally efficient flux variability analysis. BMC Bioinformatics, 11:489, 2010.

[14] J. Nocedal, S.J. Wright. Numerical Optimization. Springer, NewYork, 2006.

[15] Y. Nesterov. Introductory Lectures on Convex Optimization: A Basic Course. Kluwer, Dordrecht, 2004.

[16] M. Ahookhosh, R. Fleming, S. Ghaderi, V. Phan. Projected two-point gradient methods for large genomescalebiochemical networks. Manuscript, University of Luxembourg, 2016.

[17] M. Ahookhosh. Accelerated first-order methods for large-scale convex minimization. Submitted, 2016.

[18] Y. Nesterov. Gradient methods for minimizing composite objective function. Mathematical Programming, 140:125–161.

[19] Y. Nesterov. Universal gradient methods for convex optimization problems. Mathematical Programming, 152:381–404, 2015.

[20] P.L. Combettes, V.R. Wajs. Signal recovery by proximal forward-backward splitting. Multiscale Modeling and Simulation, 4(4):1168–1200, 2005.

[21] P. Tseng. A modified forward-backward splitting method for maximal monotone mappings. SIAM Journal on Control and Optimization, 38:431–446, 2000.

[22] R.I. Bot, C. Hendrich. A Douglas-Rachford type primal-dual method for solving inclusions with mixtures of composite and parallel-sum type monotone operators. SIAM Journal on Optimization, 23(4):2541–2565, 2013.

[23] T. Goldstein, B. ÓDonoghue, S. Setzer, R. Baraniuk. Fast alternating direction optimization methods. SIAM Journal on Imaging Sciences, 7(3):1588–1623.

[24] M. Ahookhosh. Optimal subgradient algorithms with application to large-scale linear inverse problems. Submitted, 2014.

[25] M. Ahookhosh. High-dimensional nonsmooth convex optimization via optimal subgradient methods. PhD thesis, University of Vienna, 2015.

[26] M. Ahookhosh, A. Neumaier. An optimal subgradient algorithm with subspace search for costly convex optimization problems. Submitted, 2015.

[27] M. Ahookhosh, A. Neumaier. An optimal subgradient algorithms for large-scale boundconstrained convex optimization. Submitted, 2015.

[28] M. Ahookhosh, A. Neumaier. An optimal subgradient algorithms for large-scale convex optimization in simple domains. Submitted, 2015.

[29] M. Ahookhosh, A. Neumaier. Solving nonsmooth convex optimization with complexity O(e-1/2 ). Submitted, 2015.

[30] A. Neumaier. Osga: a fast subgradient algorithm with optimal complexity. Mathematical Programming, 2015.  

[31] Y. Nesterov. Subgradient methods for huge-scale optimization problems. Mathematical Programming, 146:275–297, 2014.

[32] Y. Nesterov. Efficiency of coordinate descent methods on huge-scale optimization problems. SIAM Journal on Optimization, 22(2):341–362, 2012.

[33] P. Richtárik, M. Takáč. Parallel coordinate descent methods for big data optimization. Mathematical Programming, 156:433–484, 2016.

[34] X. Yang, S. Parthasarathy, P. Sadayappan. Fast sparse matrix-vector multiplication on GPUs: implications for graph mining. Proceedings of the VLDB Endowment, 4(4):231–242, 2011.

[35] A. Buluç, J. T. Fineman, M. Frigo, J. R. Gilbert, C. E. Leiserson. Parallel sparse matrix-vector and matrixtranspose-vector multiplication using compressed sparse blocks. In Proceedings of the Twenty-first Annual Symposium on Parallelism in Algorithms and Architectures, SPAA ’09, pages 233–244, 2009.

[36] Preconditioning Techniques for Large Linear Systems: A Survey. Journal of Computational Physics, 182:418–477, 2002.

[37] F.J. Aragon Artacho, R.M.T. Fleming. Globally convergent algorithms for finding zeros of duplomonotone mappings. Optimization Letters, 3(3):569–584, 2015.

[38] F.J. Aragon Artacho, R.M.T. Fleming, V. Phan. Accelerating the dc algorithm for smooth functions. Submitted, 2015.  

[39] H. S. Haraldsdóttir, R. M.T. Fleming. Identification of conserved moieties in metabolic networks by graph theoretical analysis of atom transition networks. to appear in PLOS Computational Biology, 2016.

[40] H. M. Le, H. S. Haraldsdottir, T. V. Phan, I. Thiele, R. M.T. Fleming. Cardinality optimisation in systems biochemistry. Manuscript, University of Luxembourg, 2016.

[41] M. Ahookhosh, K. Amini. An efficient nonmonotone trust-region method for unconstrained optimization. Numerical Algorithms, 59(4):523–540, 2012.

[42] M. Ahookhosh, K. Amini, S. Bahrami. A class of nonmonotone Armijo-type line search method for unconstrained optimization. Optimization, 61(4):387–404, 2012.

[43] M. Ahookhosh, K. Amini, M. Kimiaei. A globally convergent trust-region method for large-scale symmetric nonlinear systems. Numerical Functional Analysis and Optimization, 36:830–855, 2015.

[44] M. Ahookhosh, K. Amini, M.R. Peyghami. A nonmonotone trust-region line search method for largescale unconstrained optimization. Applied Mathematical Modelling, 36(1):478–487, 2012.

[45] M. Ahookhosh, S. Ghaderi. Two globally convergent nonmonotone trust-region methods for unconstrained optimization. Journal of Applied Mathematics and Computing, 50(1):529–555, 2016.

[46] K. Amini, M. Ahookhosh, H. Nosratipour. An inexact line search approach using modified nonmonotone strategy for unconstrained optimization. Numerical Algorithms, 66:49–78, 2014.

[47] A. Kamandi A, K. Amini, M. Ahookhosh. An improved adaptive trust-region algorithm. Optimization Letter, 2015.

[48] M. Ahookhosh, K. Amini, S. Bahrami. Two derivative-free projection approaches for systems of largescale nonlinear monotone equations. Numerical Algorithms, 64:21–42.

[49] L. Heirendt, H.H.T Liu, P. Wang. Aircraft landing gear thermo-tribomechanical model and sensitivity study. Journal Of Aircraft, 51(2):511–519, 2014.

[50] L. Heirendt, H.H.T Liu, P. Wang. Aircraft landing gear greased slider bearing steady-state thermoelastohydrodynamic concept model. Tribology International, 82:453–463, 2015.

[51] L. Heirendt. Aircraft landing gear thermo-tribomechanical model development. PhD thesis, University of Toronto, 2015. Available on request to FNR, as currently the subject of patent protection.

[52] L. Heirendt, I. Thiele, R. Fleming. Computationally improved flux variability analysis. Manuscript, University of Luxembourg, 2016.