7,065 research outputs found

    A fast branch-and-bound algorithm for non-convex quadratic integer optimization subject to linear constraints using ellipsoidal relaxations

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    We propose two exact approaches for non-convex quadratic integer minimization subject to linear constraints where lower bounds are computed by considering ellipsoidal relaxations of the feasible set. In the first approach, we intersect the ellipsoids with the feasible linear subspace. In the second approach we penalize exactly the linear constraints. We investigate the connection between both approaches theoretically. Experimental results show that the penalty approach significantly outperforms CPLEX on problems with small or medium size variable domains. © 2015 Elsevier B.V. All rights reserved

    Efficient methods of automatic calibration for rainfall-runoff modelling in the Floreon+ system

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    Calibration of rainfall-runoff model parameters is an inseparable part of hydrological simulations. To achieve more accurate results of these simulations, it is necessary to implement an efficient calibration method that provides sufficient refinement of the model parameters in a reasonable time frame. In order to perform the calibration repeatedly for large amount of data and provide results of calibrated model simulations for the flood warning process in a short time, the method also has to be automated. In this paper, several local and global optimization methods are tested for their efficiency. The main goal is to identify the most accurate method for the calibration process that provides accurate results in an operational time frame (typically less than 1 hour) to be used in the flood prediction Floreon(+) system. All calibrations were performed on the measured data during the rainfall events in 2010 in the Moravian-Silesian region (Czech Republic) using our in-house rainfall-runoff model.Web of Science27441339

    Bottom-Up Reconstruction Scenarios for (un)constrained MSSM Parameters at the LHC

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    We consider some specific inverse problem or "bottom-up" reconstruction strategies at the LHC for both general and constrained MSSM parameters, starting from a plausibly limited set of sparticle identification and mass measurements, using mainly gluino/squark cascade decays, plus eventually the lightest Higgs boson mass. For the three naturally separated sectors of: gaugino/Higgsino, squark/slepton, and Higgs parameters, we examine different step-by-step algorithms based on rather simple, entirely analytical, inverted relations between masses and basic MSSM parameters. This includes also reasonably good approximations of some of the relevant radiative correction calculations. We distinguish the constraints obtained for a general MSSM from those obtained with universality assumptions in the three different sectors. Our results are compared at different stages with the determination from more standard "top-down" fit of models to data, and finally combined into a global determination of all the relevant parameters. Our approach gives complementary information to more conventional analysis, and is not restricted to the specific LHC measurement specificities. In addition, the bottom-up renormalization group evolution of general MSSM parameters, being an important ingredient in this framework, is illustrated as a new publicly available option of the MSSM spectrum calculation code "SuSpect".Comment: 52 pages, 22 figures. Slight reorganization of sections, a few more results for the neutralino sector, one appendix added on neutralino sector calculation details. Version to appear in Phys. Rev.

    A-posteriori analysis and adaptive algorithms for the quasicontinuum method in one dimension

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    The quasicontinuum (QC) method is a coarse-graining technique for reducing the complexity of atomistic simulations in a static and quasistatic setting. In this paper we give an a-posteriori error analysis for the quasi-continuum method in one dimension. We consider atomistic models with Lennard-Jones type finite-range interactions.\ud \ud We prove that, for a stable QC solution with a sufficiently small residual, which is computed in a discrete Sobolev-type norm, there exists an exact solution of the atomistic model problem for which an a-posteriori error estimate holds. We then derive practically computable bounds on the residual and on the inf-sup constants which measure the stability of the QC solution.\ud \ud Finally, we supplement the QC method with a proximal point optimization method with local-error control. We prove that the parameters can be adjusted so that at each step of the optimization algorithm there exists an exact solution to a related atomistic problem whose distance to the numerical solution is smaller than a pre-set tolerance.\ud \ud Key words and phrases: atomistic material models, quasicontinuum method, error analysis, adaptivity, stability\ud \ud The first author acknowledges the financial support received from the European research project HPRB-CT-2002-00284: New Materials, Adaptive Systems and their Nonlinearities. Modelling, Control and Numerical Simulation, and the kind hospitality of Carlo Lovadina and Matteo Negri (University of Pavia).\ud \ud We would like to thank Nick Gould for his advice on practical optimization methods, particularly on proximal point algorithms

    Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging

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    Many graphics and vision problems can be expressed as non-linear least squares optimizations of objective functions over visual data, such as images and meshes. The mathematical descriptions of these functions are extremely concise, but their implementation in real code is tedious, especially when optimized for real-time performance on modern GPUs in interactive applications. In this work, we propose a new language, Opt (available under http://optlang.org), for writing these objective functions over image- or graph-structured unknowns concisely and at a high level. Our compiler automatically transforms these specifications into state-of-the-art GPU solvers based on Gauss-Newton or Levenberg-Marquardt methods. Opt can generate different variations of the solver, so users can easily explore tradeoffs in numerical precision, matrix-free methods, and solver approaches. In our results, we implement a variety of real-world graphics and vision applications. Their energy functions are expressible in tens of lines of code, and produce highly-optimized GPU solver implementations. These solver have performance competitive with the best published hand-tuned, application-specific GPU solvers, and orders of magnitude beyond a general-purpose auto-generated solver
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