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    SIAMJO
    2008
    108views more  SIAMJO 2008»
    Sparse SOS Relaxations for Minimizing Functions that are Summations of Small Polynomials
    13 years 7 months ago
    Sparse SOS Relaxations for Minimizing Functions that are Summations of Small Polynomials
    Download  math.ucsd.edu
    This paper discusses how to find the global minimum of functions that are summations of small polynomials ("small" means involving a small number of variables). Some spa...
    Jiawang Nie, James Demmel
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    DAC
    1999
    ACM
    166views Computer Architecture» more  DAC 1999»
    An Efficient Lyapunov Equation-Based Approach for Generating Reduced-Order Models of Interconnect
    14 years 8 months ago
    An Efficient Lyapunov Equation-Based Approach for Generating Reduced-Order Models of Interconnect
    Download  www.rle.mit.edu
    In this paper we present a new algorithm for computing reduced-order models of interconnect which utilizes the dominant controllable subspace of the system. The dominant controlla...
    Jing-Rebecca Li, Frank Wang, Jacob White
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    AMC
    2008
    86views more  AMC 2008»
    Numerical solution of stochastic Nash games with state-dependent noise for weakly coupled large-scale systems
    13 years 7 months ago
    Numerical solution of stochastic Nash games with state-dependent noise for weakly coupled large-scale systems
    Download  ir.lib.hiroshima-u.ac.jp
    This paper discusses the infinite horizon stochastic Nash games with state-dependent noise. After establishing the asymptotic structure along with the positive semidefiniteness for...
    Muneomi Sagara, Hiroaki Mukaidani, Toru Yamamoto
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    ICCS
    2007
    Springer
    174views Applied Computing» more  ICCS 2007»
    Hierarchical-Matrix Preconditioners for Parabolic Optimal Control Problems
    14 years 1 months ago
    Hierarchical-Matrix Preconditioners for Parabolic Optimal Control Problems
    Download  www.cs.uiowa.edu
    Abstract. Hierarchical (H)-matrices approximate full or sparse matrices using a hierarchical data sparse format. The corresponding H-matrix arithmetic reduces the time complexity o...
    Suely Oliveira, Fang Yang
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    NIPS
    1998
    156views Information Technology» more  NIPS 1998»
    Learning Nonlinear Dynamical Systems Using an EM Algorithm
    13 years 9 months ago
    Learning Nonlinear Dynamical Systems Using an EM Algorithm
    Download  www.cs.nyu.edu
    The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
    Zoubin Ghahramani, Sam T. Roweis
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