Optimization algorithms for the solution of the frictionless normal contact between rough surfaces

This paper revisits the fundamental equations for the solution of the frictionless unilateral normal contact problem between a rough rigid surface and a linear elastic half-plane using the boundary element method (BEM). After recasting the resulting Linear Complementarity Problem (LCP) as a convex quadratic program (QP) with nonnegative constraints, different optimization algorithms are compared for its solution: (i) a Greedy method, based on different solvers for the unconstrained linear system (Conjugate Gradient CG, Gauss-Seidel, Cholesky factorization), (ii) a constrained CG algorithm, (iii) the Alternating Direction Method of Multipliers (ADMM), and ($iv$) the Non-Negative Least Squares (NNLS) algorithm, possibly warm-started by accelerated gradient projection steps or taking advantage of a loading history. The latter method is two orders of magnitude faster than the Greedy CG method and one order of magnitude faster than the constrained CG algorithm. Finally, we propose another type of warm start based on a refined criterion for the identification of the initial trial contact domain that can be used in conjunction with all the previous optimization algorithms. This method, called Cascade Multi-Resolution (CMR), takes advantage of physical considerations regarding the scaling of the contact predictions by changing the surface resolution. The method is very efficient and accurate when applied to real or numerically generated rough surfaces, provided that their power spectral density function is of power-law type, as in case of self-similar fractal surfaces.Comment: 38 pages, 11 figure

Uncertainties in polarimetric 3D reconstructions of coronal mass ejections

This work is aimed at quantifying the uncertainties in the 3D reconstruction of the location of coronal mass ejections (CMEs) obtained with the polarization ratio technique. The method takes advantage of the different distributions along the line of sight (LOS) of total (tB) and polarized (pB) brightnesses to estimate the average location of the emitting plasma. To this end, we assumed two simple electron density distributions along the LOS (a constant density and Gaussian density profiles) for a plasma blob and synthesized the expected tB and pB for different distances $z$ of the blob from the plane of the sky (POS) and different projected altitudes $\rho$. Reconstructed locations of the blob along the LOS were thus compared with the real ones, allowing a precise determination of uncertainties in the method. Independently of the analytical density profile, when the blob is centered at a small distance from the POS (i.e. for limb CMEs) the distance from the POS starts to be significantly overestimated. Polarization ratio technique provides the LOS position of the center of mass of what we call folded density distribution, given by reflecting and summing in front of the POS the fraction of density profile located behind that plane. On the other hand, when the blob is far from the POS, but with very small projected altitudes (i.e. for halo CMEs, $\rho < 1.4$ R$_\odot$), the inferred distance from that plane is significantly underestimated. Better determination of the real blob position along the LOS is given for intermediate locations, and in particular when the blob is centered at an angle of $20^\circ$ from the POS. These result have important consequences not only for future 3D reconstruction of CMEs with polarization ratio technique, but also for the design of future coronagraphs aimed at providing a continuous monitoring of halo-CMEs for space weather prediction purposes

Super- and Sub-critical Regions in Shocks driven by Radio-Loud and Radio-Quiet CMEs

White-light coronagraphic images of Coronal Mass Ejections (CMEs) observed by SOHO/LASCO C2 have been used to estimate the density jump along the whole front of two CME-driven shocks. The two events are different in that the first one was a "radio-loud" fast CME, while the second one was a "radio quiet" slow CME. From the compression ratios inferred along the shock fronts, we estimated the Alfv\'en Mach numbers for the general case of an oblique shock. It turns out that the "radio-loud" CME shock is initially super-critical around the shock center, while later on the whole shock becomes sub-critical. On the contrary, the shock associated with the "radio-quiet" CME is sub-critical at all times. This suggests that CME-driven shocks could be efficient particle accelerators at the shock nose only at the initiation phases of the event, if and when the shock is super-critical, while at later times they lose their energy and the capability to accelerate high energetic particles.Comment: 7 pages, 5 figures. In press on the "Journal of Advanced Research", Cairo University Pres

A Decade of Coronagraphic and Spectroscopic Studies of CME-Driven Shocks

Shocks driven by Coronal Mass Ejections (CMEs) are primary agents of space weather. They can accelerate particles to high energies and can compress the magnetosphere thus setting in motion geomagnetic storms. For many years, these shocks were studied only in-situ when they crossed over spacecraft or remotely through their radio emission spectra. Neither of these two methods provides information on the spatial structure of the shock nor on its relationship to its driver, the CME. In the last decade, we have been able to not only image shocks with coronagraphs but also measure their properties remotely through the use of spectroscopic and image analysis methods. Thanks to instrumentation on STEREO and SOHO we can now image shocks (and waves) from the low corona, through the inner heliosphere, to Earth. Here, we review the progress made in imaging and analyzing CME-driven shocks and show that joint coronagraphic and spectrscopic observations are our best means to understand shock physics close to the Sun.Comment: 6 pages, 3 figure

On the convergence of stochastic MPC to terminal modes of operation

The stability of stochastic Model Predictive Control (MPC) subject to additive disturbances is often demonstrated in the literature by constructing Lyapunov-like inequalities that guarantee closed-loop performance bounds and boundedness of the state, but convergence to a terminal control law is typically not shown. In this work we use results on general state space Markov chains to find conditions that guarantee convergence of disturbed nonlinear systems to terminal modes of operation, so that they converge in probability to a priori known terminal linear feedback laws and achieve time-average performance equal to that of the terminal control law. We discuss implications for the convergence of control laws in stochastic MPC formulations, in particular we prove convergence for two formulations of stochastic MPC

A posteriori multi-stage optimal trading under transaction costs and a diversification constraint

This paper presents a simple method for a posteriori (historical) multi-variate multi-stage optimal trading under transaction costs and a diversification constraint. Starting from a given amount of money in some currency, we analyze the stage-wise optimal allocation over a time horizon with potential investments in multiple currencies and various assets. Three variants are discussed, including unconstrained trading frequency, a fixed number of total admissable trades, and the waiting of a specific time-period after every executed trade until the next trade. The developed methods are based on efficient graph generation and consequent graph search, and are evaluated quantitatively on real-world data. The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning.Comment: 25 pages, 4 figures, 6 table

A Convex Feasibility Approach to Anytime Model Predictive Control

This paper proposes to decouple performance optimization and enforcement of asymptotic convergence in Model Predictive Control (MPC) so that convergence to a given terminal set is achieved independently of how much performance is optimized at each sampling step. By embedding an explicit decreasing condition in the MPC constraints and thanks to a novel and very easy-to-implement convex feasibility solver proposed in the paper, it is possible to run an outer performance optimization algorithm on top of the feasibility solver and optimize for an amount of time that depends on the available CPU resources within the current sampling step (possibly going open-loop at a given sampling step in the extreme case no resources are available) and still guarantee convergence to the terminal set. While the MPC setup and the solver proposed in the paper can deal with quite general classes of functions, we highlight the synthesis method and show numerical results in case of linear MPC and ellipsoidal and polyhedral terminal sets.Comment: 8 page

Direct data-driven control of constrained linear parameter-varying systems: A hierarchical approach

In many nonlinear control problems, the plant can be accurately described by a linear model whose operating point depends on some measurable variables, called scheduling signals. When such a linear parameter-varying (LPV) model of the open-loop plant needs to be derived from a set of data, several issues arise in terms of parameterization, estimation, and validation of the model before designing the controller. Moreover, the way modeling errors affect the closed-loop performance is still largely unknown in the LPV context. In this paper, a direct data-driven control method is proposed to design LPV controllers directly from data without deriving a model of the plant. The main idea of the approach is to use a hierarchical control architecture, where the inner controller is designed to match a simple and a-priori specified closed-loop behavior. Then, an outer model predictive controller is synthesized to handle input/output constraints and to enhance the performance of the inner loop. The effectiveness of the approach is illustrated by means of a simulation and an experimental example. Practical implementation issues are also discussed.Comment: Preliminary version of the paper "Direct data-driven control of constrained systems" published in the IEEE Transactions on Control Systems Technolog