TY - JOUR A1 - Hastermann, Gottfried A1 - Reinhardt, Maria A1 - Klein, Rupert A1 - Reich, Sebastian T1 - Balanced data assimilation for highly oscillatory mechanical systems JF - Communications in applied mathematics and computational science : CAMCoS N2 - Data assimilation algorithms are used to estimate the states of a dynamical system using partial and noisy observations. The ensemble Kalman filter has become a popular data assimilation scheme due to its simplicity and robustness for a wide range of application areas. Nevertheless, this filter also has limitations due to its inherent assumptions of Gaussianity and linearity, which can manifest themselves in the form of dynamically inconsistent state estimates. This issue is investigated here for balanced, slowly evolving solutions to highly oscillatory Hamiltonian systems which are prototypical for applications in numerical weather prediction. It is demonstrated that the standard ensemble Kalman filter can lead to state estimates that do not satisfy the pertinent balance relations and ultimately lead to filter divergence. Two remedies are proposed, one in terms of blended asymptotically consistent time-stepping schemes, and one in terms of minimization-based postprocessing methods. The effects of these modifications to the standard ensemble Kalman filter are discussed and demonstrated numerically for balanced motions of two prototypical Hamiltonian reference systems. KW - data assimilation KW - ensemble Kalman filter KW - balanced dynamics KW - highly KW - oscillatory systems KW - Hamiltonian dynamics KW - geophysics Y1 - 2021 U6 - https://doi.org/10.2140/camcos.2021.16.119 SN - 1559-3940 SN - 2157-5452 VL - 16 IS - 1 SP - 119 EP - 154 PB - Mathematical Sciences Publishers CY - Berkeley ER - TY - JOUR A1 - Leung, Tsz Yan A1 - Leutbecher, Martin A1 - Reich, Sebastian A1 - Shepherd, Theodore G. T1 - Impact of the mesoscale range on error growth and the limits to atmospheric predictability JF - Journal of the atmospheric sciences N2 - Global numerical weather prediction (NWP) models have begun to resolve the mesoscale k(-5/3) range of the energy spectrum, which is known to impose an inherently finite range of deterministic predictability per se as errors develop more rapidly on these scales than on the larger scales. However, the dynamics of these errors under the influence of the synoptic-scale k(-3) range is little studied. Within a perfect-model context, the present work examines the error growth behavior under such a hybrid spectrum in Lorenz's original model of 1969, and in a series of identical-twin perturbation experiments using an idealized two-dimensional barotropic turbulence model at a range of resolutions. With the typical resolution of today's global NWP ensembles, error growth remains largely uniform across scales. The theoretically expected fast error growth characteristic of a k(-5/3) spectrum is seen to be largely suppressed in the first decade of the mesoscale range by the synoptic-scale k(-3) range. However, it emerges once models become fully able to resolve features on something like a 20-km scale, which corresponds to a grid resolution on the order of a few kilometers. KW - mesoscale forecasting KW - numerical weather prediction/forecasting KW - short-range prediction KW - numerical analysis/modeling Y1 - 2020 U6 - https://doi.org/10.1175/JAS-D-19-0346.1 SN - 0022-4928 SN - 1520-0469 VL - 77 IS - 11 SP - 3769 EP - 3779 PB - American Meteorological Soc. CY - Boston ER - TY - JOUR A1 - Akhmatskaya, Elena A1 - Bou-Rabee, Nawaf A1 - Reich, Sebastian T1 - A comparison of generalized hybrid Monte Carlo methods with and without momentum flip N2 - The generalized hybrid Monte Carlo (GHMC) method combines Metropolis corrected constant energy simulations with a partial random refreshment step in the particle momenta. The standard detailed balance condition requires that momenta are negated upon rejection of a molecular dynamics proposal step. The implication is a trajectory reversal upon rejection, which is undesirable when interpreting GHMC as thermostated molecular dynamics. We show that a modified detailed balance condition can be used to implement GHMC without momentum flips. The same modification can be applied to the generalized shadow hybrid Monte Carlo (GSHMC) method. Numerical results indicate that GHMC/GSHMC implementations with momentum flip display a favorable behavior in terms of sampling efficiency, i.e., the traditional GHMC/GSHMC implementations with momentum flip got the advantage of a higher acceptance rate and faster decorrelation of Monte Carlo samples. The difference is more pronounced for GHMC. We also numerically investigate the behavior of the GHMC method as a Langevin-type thermostat. We find that the GHMC method without momentum flip interferes less with the underlying stochastic molecular dynamics in terms of autocorrelation functions and it to be preferred over the GHMC method with momentum flip. The same finding applies to GSHMC. Y1 - 2009 UR - http://www.sciencedirect.com/science/journal/00219991 U6 - https://doi.org/10.1016/j.jcp.2008.12.014 SN - 0021-9991 ER - TY - JOUR A1 - Akhmatskaya, Elena A1 - Bou-Rabee, Nawaf A1 - Reich, Sebastian T1 - Erratum to "A comparison of generalized hybrid Monte Carlo methods with and without momentum flip" [J. Comput. Phys. 228 (2009), S. 2256 - 2265] N2 - The generalized hybrid Monte Carlo (GHMC) method combines Metropolis corrected constant energy simulations with a partial random refreshment step in the particle momenta. The standard detailed balance condition requires that momenta are negated upon rejection of a molecular dynamics proposal step. The implication is a trajectory reversal upon rejection, which is undesirable when interpreting GHMC as thermostated molecular dynamics. We show that a modified detailed balance condition can be used to implement GHMC without momentum flips. The same modification can be applied to the generalized shadow hybrid Monte Carlo (GSHMC) method. Numerical results indicate that GHMC/GSHMC implementations with momentum flip display a favorable behavior in terms of sampling efficiency, i.e., the traditional GHMC/GSHMC implementations with momentum flip got the advantage of a higher acceptance rate and faster decorrelation of Monte Carlo samples. The difference is more pronounced for GHMC. We also numerically investigate the behavior of the GHMC method as a Langevin-type thermostat. We find that the GHMC method without momentum flip interferes less with the underlying stochastic molecular dynamics in terms of autocorrelation functions and it to be preferred over the GHMC method with momentum flip. The same finding applies to GSHMC. Y1 - 2009 UR - http://www.sciencedirect.com/science/journal/00219991 U6 - https://doi.org/10.1016/j.jcp.2009.06.039 SN - 0021-9991 ER - TY - JOUR A1 - Leimkuhler, Benedict A1 - Reich, Sebastian T1 - A metropolis adjusted Nosé-Hoover thermostat N2 - We present a Monte Carlo technique for sampling from the canonical distribution in molecular dynamics. The method is built upon the Nose-Hoover constant temperature formulation and the generalized hybrid Monte Carlo method. In contrast to standard hybrid Monte Carlo methods only the thermostat degree of freedom is stochastically resampled during a Monte Carlo step. Y1 - 2009 UR - http://www.edpsciences.org/journal/index.cfm?edpsname=m2an U6 - https://doi.org/10.1051/M2an/2009023 SN - 0764-583X ER - TY - JOUR A1 - Reich, Sebastian T1 - A nonparametric ensemble transform method for bayesian inference JF - SIAM journal on scientific computing N2 - Many applications, such as intermittent data assimilation, lead to a recursive application of Bayesian inference within a Monte Carlo context. Popular data assimilation algorithms include sequential Monte Carlo methods and ensemble Kalman filters (EnKFs). These methods differ in the way Bayesian inference is implemented. Sequential Monte Carlo methods rely on importance sampling combined with a resampling step, while EnKFs utilize a linear transformation of Monte Carlo samples based on the classic Kalman filter. While EnKFs have proven to be quite robust even for small ensemble sizes, they are not consistent since their derivation relies on a linear regression ansatz. In this paper, we propose another transform method, which does not rely on any a priori assumptions on the underlying prior and posterior distributions. The new method is based on solving an optimal transportation problem for discrete random variables. KW - Bayesian inference KW - Monte Carlo method KW - sequential data assimilation KW - linear programming KW - resampling Y1 - 2013 U6 - https://doi.org/10.1137/130907367 SN - 1064-8275 VL - 35 IS - 4 SP - A2013 EP - A2024 PB - Society for Industrial and Applied Mathematics CY - Philadelphia ER - TY - JOUR A1 - Bergemann, Kay A1 - Gottwald, Georg A1 - Reich, Sebastian T1 - Ensemble propagation and continuous matrix factorization algorithms N2 - We consider the problem of propagating an ensemble of solutions and its characterization in terms of its mean and covariance matrix. We propose differential equations that lead to a continuous matrix factorization of the ensemble into a generalized singular value decomposition (SVD). The continuous factorization is applied to ensemble propagation under periodic rescaling (ensemble breeding) and under periodic Kalman analysis steps (ensemble Kalman filter). We also use the continuous matrix factorization to perform a re-orthogonalization of the ensemble after each time-step and apply the resulting modified ensemble propagation algorithm to the ensemble Kalman filter. Results from the Lorenz-96 model indicate that the re-orthogonalization of the ensembles leads to improved filter performance. Y1 - 2009 UR - http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291477-870X U6 - https://doi.org/10.1002/qj.457 SN - 0035-9009 ER - TY - JOUR A1 - Cotter, Colin J. A1 - Ham, David A. A1 - Pain, Christopher C. A1 - Reich, Sebastian T1 - LBB stability of a mixed Galerkin finite element pair for fluid flow simulations N2 - We introduce a new mixed finite element for solving the 2- and 3-dimensional wave equations and equations of incompressible flow. The element, which we refer to as P1(D)-P2, uses discontinuous piecewise linear functions for velocity and continuous piecewise quadratic functions for pressure. The aim of introducing the mixed formulation is to produce a new flexible element choice for triangular and tetrahedral meshes which satisfies the LBB stability condition and hence has no spurious zero-energy modes. The advantage of this particular element choice is that the mass matrix for velocity is block diagonal so it can be trivially inverted; it also allows the order of the pressure to be increased to quadratic whilst maintaining LBB stability which has benefits in geophysical applications with Coriolis forces. We give a normal mode analysis of the semi-discrete wave equation in one dimension which shows that the element pair is stable, and demonstrate that the element is stable with numerical integrations of the wave equation in two dimensions, an analysis of the resultant discrete Laplace operator in two and three dimensions on various meshes which shows that the element pair does not have any spurious modes. We provide convergence tests for the element pair which confirm that the element is stable since the convergence rate of the numerical solution is quadratic. Y1 - 2009 UR - http://www.sciencedirect.com/science/journal/00219991 U6 - https://doi.org/10.1016/j.jcp.2008.09.014 SN - 0021-9991 ER - TY - JOUR A1 - Bergemann, Kay A1 - Reich, Sebastian T1 - A localization technique for ensemble Kalman filters N2 - Ensemble Kalman filter techniques are widely used to assimilate observations into dynamical models. The phase- space dimension is typically much larger than the number of ensemble members, which leads to inaccurate results in the computed covariance matrices. These inaccuracies can lead, among other things, to spurious long-range correlations, which can be eliminated by Schur-product-based localization techniques. In this article, we propose a new technique for implementing such localization techniques within the class of ensemble transform/square-root Kalman filters. Our approach relies on a continuous embedding of the Kalman filter update for the ensemble members, i.e. we state an ordinary differential equation (ODE) with solutions that, over a unit time interval, are equivalent to the Kalman filter update. The ODE formulation forms a gradient system with the observations as a cost functional. Besides localization, the new ODE ensemble formulation should also find useful application in the context of nonlinear observation operators and observations that arrive continuously in time. Y1 - 2010 UR - http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1477-870X U6 - https://doi.org/10.1002/Qj.591 SN - 0035-9009 ER - TY - JOUR A1 - Bergemann, Kay A1 - Reich, Sebastian T1 - A mollified ensemble Kalman filter N2 - It is well recognized that discontinuous analysis increments of sequential data assimilation systems, such as ensemble Kalman filters, might lead to spurious high-frequency adjustment processes in the model dynamics. Various methods have been devised to spread out the analysis increments continuously over a fixed time interval centred about the analysis time. Among these techniques are nudging and incremental analysis updates (IAU). Here we propose another alternative, which may be viewed as a hybrid of nudging and IAU and which arises naturally from a recently proposed continuous formulation of the ensemble Kalman analysis step. A new slow-fast extension of the popular Lorenz-96 model is introduced to demonstrate the properties of the proposed mollified ensemble Kalman filter. Y1 - 2010 UR - http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1477-870X U6 - https://doi.org/10.1002/Qj.672 SN - 0035-9009 ER - TY - JOUR A1 - Shin, Seoleun A1 - Zöller, Gert A1 - Holschneider, Matthias A1 - Reich, Sebastian T1 - A multigrid solver for modeling complex interseismic stress fields JF - Computers & geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology N2 - We develop a multigrid, multiple time stepping scheme to reduce computational efforts for calculating complex stress interactions in a strike-slip 2D planar fault for the simulation of seismicity. The key elements of the multilevel solver are separation of length scale, grid-coarsening, and hierarchy. In this study the complex stress interactions are split into two parts: the first with a small contribution is computed on a coarse level, and the rest for strong interactions is on a fine level. This partition leads to a significant reduction of the number of computations. The reduction of complexity is even enhanced by combining the multigrid with multiple time stepping. Computational efficiency is enhanced by a factor of 10 while retaining a reasonable accuracy, compared to the original full matrix-vortex multiplication. The accuracy of solution and computational efficiency depend on a given cut-off radius that splits multiplications into the two parts. The multigrid scheme is constructed in such a way that it conserves stress in the entire half-space. KW - Multigrid KW - Multiple time stepping KW - Strike-slip fault model Y1 - 2011 U6 - https://doi.org/10.1016/j.cageo.2010.11.011 SN - 0098-3004 VL - 37 IS - 8 SP - 1075 EP - 1082 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Grewe, Volker A1 - Brinkop, Sabine A1 - Joeckel, Patrick A1 - Shin, Seoleun A1 - Reich, Sebastian A1 - Yserentant, Harry T1 - On the theory of mass conserving transformations for Lagrangian methods in 3D atmosphere-chemistry models JF - Meteorologische Zeitschrift KW - Lagrangian modelling KW - chemistry KW - transformations Y1 - 2014 U6 - https://doi.org/10.1127/0941-2948/2014/0552 SN - 0941-2948 SN - 1610-1227 VL - 23 IS - 4 SP - 441 EP - 447 PB - Schweizerbart CY - Stuttgart ER - TY - JOUR A1 - Shin, Seoleun A1 - Reich, Sebastian A1 - Frank, Jason T1 - Hydrostatic Hamiltonian particle-mesh (HPM) methods for atmospheric modelling JF - Quarterly journal of the Royal Meteorological Society N2 - We develop a hydrostatic Hamiltonian particle-mesh (HPM) method for efficient long-term numerical integration of the atmosphere. In the HPM method, the hydrostatic approximation is interpreted as a holonomic constraint for the vertical position of particles. This can be viewed as defining a set of vertically buoyant horizontal meshes, with the altitude of each mesh point determined so as to satisfy the hydrostatic balance condition and with particles modelling horizontal advection between the moving meshes. We implement the method in a vertical-slice model and evaluate its performance for the simulation of idealized linear and nonlinear orographic flow in both dry and moist environments. The HPM method is able to capture the basic features of the gravity wave to a degree of accuracy comparable with that reported in the literature. The numerical solution in the moist experiment indicates that the influence of moisture on wave characteristics is represented reasonably well and the reduction of momentum flux is in good agreement with theoretical analysis. KW - conservative discretization KW - Lagrangian modeling KW - holonomic constraints KW - fluid mechanics Y1 - 2012 U6 - https://doi.org/10.1002/qj.982 SN - 0035-9009 VL - 138 IS - 666 SP - 1388 EP - 1399 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Reich, Sebastian T1 - A dynamical systems framework for intermittent data assimilation JF - BIT : numerical mathematics ; the leading applied mathematics journal for all computational mathematicians N2 - We consider the problem of discrete time filtering (intermittent data assimilation) for differential equation models and discuss methods for its numerical approximation. The focus is on methods based on ensemble/particle techniques and on the ensemble Kalman filter technique in particular. We summarize as well as extend recent work on continuous ensemble Kalman filter formulations, which provide a concise dynamical systems formulation of the combined dynamics-assimilation problem. Possible extensions to fully nonlinear ensemble/particle based filters are also outlined using the framework of optimal transportation theory. KW - Data assimilation KW - Ensemble Kalman filter KW - Dynamical systems KW - Nonlinear filters KW - Optimal transportation Y1 - 2011 U6 - https://doi.org/10.1007/s10543-010-0302-4 SN - 0006-3835 VL - 51 IS - 1 SP - 235 EP - 249 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Skeel, R. D. A1 - Reich, Sebastian T1 - Corrected potential energy functions for constrained molecular dynamics JF - European physical journal special topics N2 - Atomic oscillations present in classical molecular dynamics restrict the step size that can be used. Multiple time stepping schemes offer only modest improvements, and implicit integrators are costly and inaccurate. The best approach may be to actually remove the highest frequency oscillations by constraining bond lengths and bond angles, thus permitting perhaps a 4-fold increase in the step size. However, omitting degrees of freedom produces errors in statistical averages, and rigid angles do not bend for strong excluded volume forces. These difficulties can be addressed by an enhanced treatment of holonomic constrained dynamics using ideas from papers of Fixman (1974) and Reich (1995, 1999). In particular, the 1995 paper proposes the use of "flexible" constraints, and the 1999 paper uses a modified potential energy function with rigid constraints to emulate flexible constraints. Presented here is a more direct and rigorous derivation of the latter approach, together with justification for the use of constraints in molecular modeling. With rigor comes limitations, so practical compromises are proposed: simplifications of the equations and their judicious application when assumptions are violated. Included are suggestions for new approaches. Y1 - 2011 U6 - https://doi.org/10.1140/epjst/e2011-01518-8 SN - 1951-6355 VL - 200 IS - 1 SP - 55 EP - 72 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Amezcua, Javier A1 - Ide, Kayo A1 - Kalnay, Eugenia A1 - Reich, Sebastian T1 - Ensemble transform Kalman-Bucy filters JF - Quarterly journal of the Royal Meteorological Society N2 - Two recent works have adapted the Kalman-Bucy filter into an ensemble setting. In the first formulation, the ensemble of perturbations is updated by the solution of an ordinary differential equation (ODE) in pseudo-time, while the mean is updated as in the standard Kalman filter. In the second formulation, the full ensemble is updated in the analysis step as the solution of single set of ODEs in pseudo-time. Neither requires matrix inversions except for the frequently diagonal observation error covariance. We analyse the behaviour of the ODEs involved in these formulations. We demonstrate that they stiffen for large magnitudes of the ratio of background error to observational error variance, and that using the integration scheme proposed in both formulations can lead to failure. A numerical integration scheme that is both stable and is not computationally expensive is proposed. We develop transform-based alternatives for these Bucy-type approaches so that the integrations are computed in ensemble space where the variables are weights (of dimension equal to the ensemble size) rather than model variables. Finally, the performance of our ensemble transform Kalman-Bucy implementations is evaluated using three models: the 3-variable Lorenz 1963 model, the 40-variable Lorenz 1996 model, and a medium complexity atmospheric general circulation model known as SPEEDY. The results from all three models are encouraging and warrant further exploration of these assimilation techniques. KW - Kalman-Bucy Filter KW - Ensemble Kalman Filter KW - stiff ODE KW - weight-based formulations Y1 - 2014 U6 - https://doi.org/10.1002/qj.2186 SN - 0035-9009 SN - 1477-870X VL - 140 IS - 680 SP - 995 EP - 1004 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Bergemann, Kay A1 - Reich, Sebastian T1 - An ensemble Kalman-Bucy filter for continuous data assimilation JF - Meteorologische Zeitschrift N2 - The ensemble Kalman filter has emerged as a promising filter algorithm for nonlinear differential equations subject to intermittent observations. In this paper, we extend the well-known Kalman-Bucy filter for linear differential equations subject to continous observations to the ensemble setting and nonlinear differential equations. The proposed filter is called the ensemble Kalman-Bucy filter and its performance is demonstrated for a simple mechanical model (Langevin dynamics) subject to incremental observations of its velocity. Y1 - 2012 U6 - https://doi.org/10.1127/0941-2948/2012/0307 SN - 0941-2948 VL - 21 IS - 3 SP - 213 EP - 219 PB - Schweizerbart CY - Stuttgart ER - TY - JOUR A1 - Reich, Sebastian T1 - A Gaussian-mixture ensemble transform filter JF - Quarterly journal of the Royal Meteorological Society N2 - We generalize the popular ensemble Kalman filter to an ensemble transform filter, in which the prior distribution can take the form of a Gaussian mixture or a Gaussian kernel density estimator. The design of the filter is based on a continuous formulation of the Bayesian filter analysis step. We call the new filter algorithm the ensemble Gaussian-mixture filter (EGMF). The EGMF is implemented for three simple test problems (Brownian dynamics in one dimension, Langevin dynamics in two dimensions and the three-dimensional Lorenz-63 model). It is demonstrated that the EGMF is capable of tracking systems with non-Gaussian uni- and multimodal ensemble distributions. KW - data assimilation KW - ensemble Kalman filter KW - nonlinear filtering KW - Gaussian mixtures KW - Gaussian kernel estimators Y1 - 2012 U6 - https://doi.org/10.1002/qj.898 SN - 0035-9009 VL - 138 IS - 662 SP - 222 EP - 233 PB - Wiley-Blackwell CY - Malden ER - TY - JOUR A1 - Acevedo, Walter A1 - Reich, Sebastian A1 - Cubasch, Ulrich T1 - Towards the assimilation of tree-ring-width records using ensemble Kalman filtering techniques JF - Climate dynamics : observational, theoretical and computational research on the climate system N2 - This paper investigates the applicability of the Vaganov–Shashkin–Lite (VSL) forward model for tree-ring-width chronologies as observation operator within a proxy data assimilation (DA) setting. Based on the principle of limiting factors, VSL combines temperature and moisture time series in a nonlinear fashion to obtain simulated TRW chronologies. When used as observation operator, this modelling approach implies three compounding, challenging features: (1) time averaging, (2) “switching recording” of 2 variables and (3) bounded response windows leading to “thresholded response”. We generate pseudo-TRW observations from a chaotic 2-scale dynamical system, used as a cartoon of the atmosphere-land system, and attempt to assimilate them via ensemble Kalman filtering techniques. Results within our simplified setting reveal that VSL’s nonlinearities may lead to considerable loss of assimilation skill, as compared to the utilization of a time-averaged (TA) linear observation operator. In order to understand this undesired effect, we embed VSL’s formulation into the framework of fuzzy logic (FL) theory, which thereby exposes multiple representations of the principle of limiting factors. DA experiments employing three alternative growth rate functions disclose a strong link between the lack of smoothness of the growth rate function and the loss of optimality in the estimate of the TA state. Accordingly, VSL’s performance as observation operator can be enhanced by resorting to smoother FL representations of the principle of limiting factors. This finding fosters new interpretations of tree-ring-growth limitation processes. KW - Proxy forward modeling KW - Data assimilation KW - Fuzzy logic KW - Ensemble Kalman filter KW - Paleoclimate reconstruction Y1 - 2016 U6 - https://doi.org/10.1007/s00382-015-2683-1 SN - 0930-7575 SN - 1432-0894 VL - 46 SP - 1909 EP - 1920 PB - Springer CY - New York ER - TY - JOUR A1 - Gregory, A. A1 - Cotter, C. J. A1 - Reich, Sebastian T1 - MULTILEVEL ENSEMBLE TRANSFORM PARTICLE FILTERING JF - SIAM journal on scientific computing N2 - This paper extends the multilevel Monte Carlo variance reduction technique to nonlinear filtering. In particular, multilevel Monte Carlo is applied to a certain variant of the particle filter, the ensemble transform particle filter (EPTF). A key aspect is the use of optimal transport methods to re-establish correlation between coarse and fine ensembles after resampling; this controls the variance of the estimator. Numerical examples present a proof of concept of the effectiveness of the proposed method, demonstrating significant computational cost reductions (relative to the single-level ETPF counterpart) in the propagation of ensembles. KW - multilevel Monte Carlo KW - sequential data assimilation KW - optimal transport Y1 - 2016 U6 - https://doi.org/10.1137/15M1038232 SN - 1064-8275 SN - 1095-7197 VL - 38 SP - A1317 EP - A1338 PB - Society for Industrial and Applied Mathematics CY - Philadelphia ER -