This page contains the publications for the OptSys proposal.
 B. O. Palsson. Systems Biology: Constraint-based Reconstruction and Analysis. Cambridge University Press, NY, 2015.
 T. Dandekar, A. Fieselmann, S. Majeed, Z. Ahmed. Software applications toward quantitative metabolic flux analysis and modeling. Briefings in Bioinformatics, pages 1–17, 2012.
 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.
 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.
 I. Thiele, B.O. Palsson. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature Protocols, 5:93–121, 2010.
 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.
 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.
 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.
 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.
 J.D. Orth, I. Thiele, B.O. Palsson. What is flux balance analysis? Nature Biotechnology, 28(3):245–248, 2010.
 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.
 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.
 S. Gudmundsson, I. Thiele. Computationally efficient flux variability analysis. BMC Bioinformatics, 11:489, 2010.
 J. Nocedal, S.J. Wright. Numerical Optimization. Springer, NewYork, 2006.
 Y. Nesterov. Introductory Lectures on Convex Optimization: A Basic Course. Kluwer, Dordrecht, 2004.
 M. Ahookhosh, R. Fleming, S. Ghaderi, V. Phan. Projected two-point gradient methods for large genomescalebiochemical networks. Manuscript, University of Luxembourg, 2016.
 M. Ahookhosh. Accelerated first-order methods for large-scale convex minimization. Submitted, 2016.
 Y. Nesterov. Gradient methods for minimizing composite objective function. Mathematical Programming, 140:125–161.
 Y. Nesterov. Universal gradient methods for convex optimization problems. Mathematical Programming, 152:381–404, 2015.
 P.L. Combettes, V.R. Wajs. Signal recovery by proximal forward-backward splitting. Multiscale Modeling and Simulation, 4(4):1168–1200, 2005.
 P. Tseng. A modified forward-backward splitting method for maximal monotone mappings. SIAM Journal on Control and Optimization, 38:431–446, 2000.
 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.
 T. Goldstein, B. ÓDonoghue, S. Setzer, R. Baraniuk. Fast alternating direction optimization methods. SIAM Journal on Imaging Sciences, 7(3):1588–1623.
 M. Ahookhosh. Optimal subgradient algorithms with application to large-scale linear inverse problems. Submitted, 2014.
 M. Ahookhosh. High-dimensional nonsmooth convex optimization via optimal subgradient methods. PhD thesis, University of Vienna, 2015.
 M. Ahookhosh, A. Neumaier. An optimal subgradient algorithm with subspace search for costly convex optimization problems. Submitted, 2015.
 M. Ahookhosh, A. Neumaier. An optimal subgradient algorithms for large-scale boundconstrained convex optimization. Submitted, 2015.
 M. Ahookhosh, A. Neumaier. An optimal subgradient algorithms for large-scale convex optimization in simple domains. Submitted, 2015.
 M. Ahookhosh, A. Neumaier. Solving nonsmooth convex optimization with complexity O(e-1/2 ). Submitted, 2015.
 A. Neumaier. Osga: a fast subgradient algorithm with optimal complexity. Mathematical Programming, 2015.
 Y. Nesterov. Subgradient methods for huge-scale optimization problems. Mathematical Programming, 146:275–297, 2014.
 Y. Nesterov. Efficiency of coordinate descent methods on huge-scale optimization problems. SIAM Journal on Optimization, 22(2):341–362, 2012.
 P. Richtárik, M. Takáč. Parallel coordinate descent methods for big data optimization. Mathematical Programming, 156:433–484, 2016.
 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.
 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.
 Preconditioning Techniques for Large Linear Systems: A Survey. Journal of Computational Physics, 182:418–477, 2002.
 F.J. Aragon Artacho, R.M.T. Fleming. Globally convergent algorithms for finding zeros of duplomonotone mappings. Optimization Letters, 3(3):569–584, 2015.
 F.J. Aragon Artacho, R.M.T. Fleming, V. Phan. Accelerating the dc algorithm for smooth functions. Submitted, 2015.
 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.
 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.
 M. Ahookhosh, K. Amini. An efficient nonmonotone trust-region method for unconstrained optimization. Numerical Algorithms, 59(4):523–540, 2012.
 M. Ahookhosh, K. Amini, S. Bahrami. A class of nonmonotone Armijo-type line search method for unconstrained optimization. Optimization, 61(4):387–404, 2012.
 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.
 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.
 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.
 K. Amini, M. Ahookhosh, H. Nosratipour. An inexact line search approach using modified nonmonotone strategy for unconstrained optimization. Numerical Algorithms, 66:49–78, 2014.
 A. Kamandi A, K. Amini, M. Ahookhosh. An improved adaptive trust-region algorithm. Optimization Letter, 2015.
 M. Ahookhosh, K. Amini, S. Bahrami. Two derivative-free projection approaches for systems of largescale nonlinear monotone equations. Numerical Algorithms, 64:21–42.
 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.
 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.
 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.
 L. Heirendt, I. Thiele, R. Fleming. Computationally improved flux variability analysis. Manuscript, University of Luxembourg, 2016.