19,219 research outputs found

    Order reduction approaches for the algebraic Riccati equation and the LQR problem

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    We explore order reduction techniques for solving the algebraic Riccati equation (ARE), and investigating the numerical solution of the linear-quadratic regulator problem (LQR). A classical approach is to build a surrogate low dimensional model of the dynamical system, for instance by means of balanced truncation, and then solve the corresponding ARE. Alternatively, iterative methods can be used to directly solve the ARE and use its approximate solution to estimate quantities associated with the LQR. We propose a class of Petrov-Galerkin strategies that simultaneously reduce the dynamical system while approximately solving the ARE by projection. This methodology significantly generalizes a recently developed Galerkin method by using a pair of projection spaces, as it is often done in model order reduction of dynamical systems. Numerical experiments illustrate the advantages of the new class of methods over classical approaches when dealing with large matrices

    Collapsibility to a subcomplex of a given dimension is NP-complete

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    In this paper we extend the works of Tancer and of Malgouyres and Franc\'es, showing that (d,k)(d,k)-collapsibility is NP-complete for d≥k+2d\geq k+2 except (2,0)(2,0). By (d,k)(d,k)-collapsibility we mean the following problem: determine whether a given dd-dimensional simplicial complex can be collapsed to some kk-dimensional subcomplex. The question of establishing the complexity status of (d,k)(d,k)-collapsibility was asked by Tancer, who proved NP-completeness of (d,0)(d,0) and (d,1)(d,1)-collapsibility (for d≥3d\geq 3). Our extended result, together with the known polynomial-time algorithms for (2,0)(2,0) and d=k+1d=k+1, answers the question completely

    Changepoint Detection over Graphs with the Spectral Scan Statistic

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    We consider the change-point detection problem of deciding, based on noisy measurements, whether an unknown signal over a given graph is constant or is instead piecewise constant over two connected induced subgraphs of relatively low cut size. We analyze the corresponding generalized likelihood ratio (GLR) statistics and relate it to the problem of finding a sparsest cut in a graph. We develop a tractable relaxation of the GLR statistic based on the combinatorial Laplacian of the graph, which we call the spectral scan statistic, and analyze its properties. We show how its performance as a testing procedure depends directly on the spectrum of the graph, and use this result to explicitly derive its asymptotic properties on few significant graph topologies. Finally, we demonstrate both theoretically and by simulations that the spectral scan statistic can outperform naive testing procedures based on edge thresholding and χ2\chi^2 testing
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