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Tong Zhou - One of the best experts on this subject based on the ideXlab platform.

  • Spatially Interconnected Systems Positive on a Quadratically Constrained Frequency Domain
    IFAC Proceedings Volumes, 2020
    Co-Authors: Tong Zhou
    Abstract:

    Abstract Generalized Positiveness is investigated in this paper for a spatially interconnected system (SIS). Results of Zhou [2009] are extended to a more general frequency domain and more complicated requirements on the frequency response of a SIS. Based on an alternative characterization of the desirable frequency domain, a linear matrix inequality (LMI) based sufficient condition is derived for the weighted Positiveness of a SIS over a frequency domain described by several matrix valued quadratic functions. Moreover, using the separability property of convex sets, situations have also been clarified under which this sufficient condition becomes also necessary.

  • generalized Positiveness of spatially interconnected systems over quadratically constrained frequency domains
    Systems & Control Letters, 2012
    Co-Authors: Tong Zhou
    Abstract:

    Abstract Generalized Positiveness is investigated in this paper for a spatially interconnected system (SIS). Results of [7] are corrected and extended to a more general frequency domain and to more complicated requirements on the frequency response of a SIS. Based on an equivalent characterization of the interested frequency domain, a linear matrix inequality (LMI) based sufficient condition is derived for the generalized Positiveness of a SIS over a frequency domain described by several matrix valued quadratic functions. Moreover, using the separability property of convex sets, situations have also been clarified under which this sufficient condition becomes also necessary.

Vikram Sharma - One of the best experts on this subject based on the ideXlab platform.

  • improved bounds on absolute Positiveness of multivariate polynomials
    Journal of Symbolic Computation, 2019
    Co-Authors: Swaroop N. Prabhakar, Vikram Sharma
    Abstract:

    Abstract A multivariate polynomial F ( x 1 , x 2 , … , x n ) is said to be absolutely positive from a real number B if F and all its partial derivatives are non-negative for x 1 , x 2 , … , x n ≥ B . One of the well known bounds on absolute Positiveness in the literature is due to Hong. His bound is dependent on the largest number in a certain sequence of radicals defined using the absolute value of the coefficients of the polynomial. In the univariate setting, a bound due to Lagrange considers the first and the second largest numbers in the same radical sequence and is shown by Collins to be better than Hong's bound. In the 1930's, Westerfield had proposed a bound that considers every value in the same radical sequence and improves on Lagrange's bound. In this paper, we provide a hierarchy of bounds generalizing Westerfield's bound to the multivariate setting. One of the bounds in this hierarchy is a generalization of Lagrange's bound. All the bounds are strict quantitative improvements over Hong's bound. We also give an algorithm to compute the multivariate Lagrange bound. The running time of this algorithm matches the running time of the best known algorithm to compute Hong's bound. The algorithm uses the range tree data structure to implement orthogonal range querying. Relying on a result of Fredman (1984), we show that the efficiency of this algorithm cannot be improved by using any other data structure for orthogonal range querying.

  • improved bounds on absolute Positiveness of multivariate polynomials
    International Symposium on Symbolic and Algebraic Computation, 2017
    Co-Authors: Swaroop N. Prabhakar, Vikram Sharma
    Abstract:

    A multivariate polynomial F(x1,x2,...,xn) is said to be absolutely positive from a real number B if F and all its partial derivatives are non-negative for x1,x2,...,xn ≥ B. One of the well known bounds on absolute Positiveness in the literature is due to Hong. His bound is dependent on the first maximum of a certain sequence of radicals defined using the absolute value of the coefficients of the polynomial. In the univariate setting, a bound due to Lagrange considers the first and the second maximum in the same radical sequence and is shown by Collins to be better than Hong's bound. In the 1930's, Westerfield had proposed a bound that consides every value in the same radical sequence and improves on Lagrange's bound. In this paper, we provide a generalization of Westerfield's bound to the multivariate setting. As a specialization of this bound, we also derive a generalization of Lagrange's bound, which is a strict improvement upon Hong's bound. Finally, we give an algorithm to compute this improved bound. The running time of this algorithm matches the running time of the best known algorithm to compute Hong's bound.

  • CASC - A Lower Bound for Computing Lagrange’s Real Root Bound
    Computer Algebra in Scientific Computing, 2016
    Co-Authors: Swaroop N. Prabhakar, Vikram Sharma
    Abstract:

    In this paper, we study a bound on the real roots of a polynomial by Lagrange. From known results in the literature, it follows that Lagrange’s bound is also a bound on the absolute Positiveness of a polynomial. A simple \(O(n\log n)\) algorithm described in Mehlhorn-Ray (2010) can be used to compute the bound. Our main result is that this is optimal in the real RAM model. Our paper explores the tradeoff between improving the quality of bounds on absolute Positiveness and their computational complexity.

  • bounds on absolute Positiveness of multivariate polynomials
    Journal of Symbolic Computation, 2010
    Co-Authors: Prashant Batra, Vikram Sharma
    Abstract:

    We propose and study a weighting framework for obtaining bounds on absolute Positiveness of multivariate polynomials. It is shown that a well-known bound B"G by Hong is obtainable in this framework, and w.r.t. any bound in this framework B"G has a multiplicative overestimation which is at most linear in the number of variables. We also propose a general method to algorithmically improve any bound within the framework. In the univariate case, we derive the minimum number of weights necessary to obtain a bound with limited overestimation w.r.t. the absolute Positiveness of the polynomial.

Petr Augusta - One of the best experts on this subject based on the ideXlab platform.

Swaroop N. Prabhakar - One of the best experts on this subject based on the ideXlab platform.

  • improved bounds on absolute Positiveness of multivariate polynomials
    Journal of Symbolic Computation, 2019
    Co-Authors: Swaroop N. Prabhakar, Vikram Sharma
    Abstract:

    Abstract A multivariate polynomial F ( x 1 , x 2 , … , x n ) is said to be absolutely positive from a real number B if F and all its partial derivatives are non-negative for x 1 , x 2 , … , x n ≥ B . One of the well known bounds on absolute Positiveness in the literature is due to Hong. His bound is dependent on the largest number in a certain sequence of radicals defined using the absolute value of the coefficients of the polynomial. In the univariate setting, a bound due to Lagrange considers the first and the second largest numbers in the same radical sequence and is shown by Collins to be better than Hong's bound. In the 1930's, Westerfield had proposed a bound that considers every value in the same radical sequence and improves on Lagrange's bound. In this paper, we provide a hierarchy of bounds generalizing Westerfield's bound to the multivariate setting. One of the bounds in this hierarchy is a generalization of Lagrange's bound. All the bounds are strict quantitative improvements over Hong's bound. We also give an algorithm to compute the multivariate Lagrange bound. The running time of this algorithm matches the running time of the best known algorithm to compute Hong's bound. The algorithm uses the range tree data structure to implement orthogonal range querying. Relying on a result of Fredman (1984), we show that the efficiency of this algorithm cannot be improved by using any other data structure for orthogonal range querying.

  • improved bounds on absolute Positiveness of multivariate polynomials
    International Symposium on Symbolic and Algebraic Computation, 2017
    Co-Authors: Swaroop N. Prabhakar, Vikram Sharma
    Abstract:

    A multivariate polynomial F(x1,x2,...,xn) is said to be absolutely positive from a real number B if F and all its partial derivatives are non-negative for x1,x2,...,xn ≥ B. One of the well known bounds on absolute Positiveness in the literature is due to Hong. His bound is dependent on the first maximum of a certain sequence of radicals defined using the absolute value of the coefficients of the polynomial. In the univariate setting, a bound due to Lagrange considers the first and the second maximum in the same radical sequence and is shown by Collins to be better than Hong's bound. In the 1930's, Westerfield had proposed a bound that consides every value in the same radical sequence and improves on Lagrange's bound. In this paper, we provide a generalization of Westerfield's bound to the multivariate setting. As a specialization of this bound, we also derive a generalization of Lagrange's bound, which is a strict improvement upon Hong's bound. Finally, we give an algorithm to compute this improved bound. The running time of this algorithm matches the running time of the best known algorithm to compute Hong's bound.

  • CASC - A Lower Bound for Computing Lagrange’s Real Root Bound
    Computer Algebra in Scientific Computing, 2016
    Co-Authors: Swaroop N. Prabhakar, Vikram Sharma
    Abstract:

    In this paper, we study a bound on the real roots of a polynomial by Lagrange. From known results in the literature, it follows that Lagrange’s bound is also a bound on the absolute Positiveness of a polynomial. A simple \(O(n\log n)\) algorithm described in Mehlhorn-Ray (2010) can be used to compute the bound. Our main result is that this is optimal in the real RAM model. Our paper explores the tradeoff between improving the quality of bounds on absolute Positiveness and their computational complexity.

Jiang Ming - One of the best experts on this subject based on the ideXlab platform.